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ckpts/universal/global_step20/zero/18.post_attention_layernorm.weight/exp_avg.pt

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ckpts/universal/global_step20/zero/4.mlp.dense_4h_to_h.weight/exp_avg_sq.pt

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ckpts/universal/global_step20/zero/4.mlp.dense_4h_to_h.weight/fp32.pt

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lm-evaluation-harness/wandb/run-20240514_114444-rasgu64a/files/config.yaml

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lm-evaluation-harness/wandb/run-20240514_114444-rasgu64a/files/output.log

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+
+2024-05-14:11:44:44,636 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-14:11:44:49,674 INFO     [__main__.py:335] Selected Tasks: ['indiccopa-hi']
+2024-05-14:11:44:49,676 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-14:11:44:49,676 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/data/cronscript/ckpts//hf_ckpt//global_step100'}
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/core/register.py:145: UserWarning: "hpu:X" notation is not supported by Gaudi PyTorch intergration bridge. Please change to "hpu" without index (Triggered internally at /npu-stack/pytorch-integration/pytorch_helpers/lazy_to_backend.cpp:53.)
+  return func(*args, **kwargs)
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
+  warnings.warn(
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/__init__.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
+  warnings.warn(
+You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
+2024-05-14:11:44:58,230 WARNING  [task.py:763] [Task: indiccopa-hi] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-14:11:44:58,230 WARNING  [task.py:775] [Task: indiccopa-hi] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+[2024-05-14 11:44:57,815] [INFO] [real_accelerator.py:178:get_accelerator] Setting ds_accelerator to hpu (auto detect)
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for ai4bharat/IndicCOPA contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/ai4bharat/IndicCOPA
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+2024-05-14:11:45:03,691 WARNING  [task.py:322] [Task: indiccopa-hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-05-14:11:45:03,692 WARNING  [task.py:322] [Task: indiccopa-hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-05-14:11:45:03,711 INFO     [task.py:395] Building contexts for indiccopa-hi on rank 2...
+100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 56/56 [00:00<00:00, 105469.70it/s]
+2024-05-14:11:45:05,430 INFO     [evaluator.py:379] Running loglikelihood requests
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
+  warnings.warn(
+Passed argument batch_size = auto:1. Detecting largest batch size
+Determined largest batch size: 64

lm-evaluation-harness/wandb/run-20240514_114444-rasgu64a/files/requirements.txt

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lm-evaluation-harness/wandb/run-20240523_061244-lrp73hbe/files/output.log

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+
+2024-05-23:06:12:44,732 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-23:06:12:54,111 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande']
+2024-05-23:06:12:54,112 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-23:06:12:54,112 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step10000'}
+2024-05-23:06:12:56,415 INFO     [huggingface.py:164] Using device 'cuda'
+Traceback (most recent call last):
+  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
+    cli_evaluate()
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
+    return fn(*args, **kwargs)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
+    self._get_config(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
+    self._config = transformers.AutoConfig.from_pretrained(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 934, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 632, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 689, in _get_config_dict
+    resolved_config_file = cached_file(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 370, in cached_file
+    raise EnvironmentError(
+OSError: /mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step10000 does not appear to have a file named config.json. Checkout 'https://huggingface.co//mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step10000/tree/main' for available files.

lm-evaluation-harness/wandb/run-20240523_061244-lrp73hbe/files/wandb-summary.json

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+{"_wandb": {"runtime": 11}}

lm-evaluation-harness/wandb/run-20240523_130407-wvnshpcy/files/config.yaml

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+
+_wandb:
+  desc: null
+  value:
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+    cli_version: 0.17.0
+    framework: huggingface
+    huggingface_version: 4.41.1
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1716469447
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lm-evaluation-harness/wandb/run-20240523_130407-wvnshpcy/files/output.log

@@ -0,0 +1,34 @@
+
+2024-05-23:13:04:08,203 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-23:13:04:16,734 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande']
+2024-05-23:13:04:16,735 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-23:13:04:16,736 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step12000'}
+2024-05-23:13:04:19,146 INFO     [huggingface.py:164] Using device 'cuda'
+Traceback (most recent call last):
+  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
+    cli_evaluate()
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
+    return fn(*args, **kwargs)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
+    self._get_config(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
+    self._config = transformers.AutoConfig.from_pretrained(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 934, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 632, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 689, in _get_config_dict
+    resolved_config_file = cached_file(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 370, in cached_file
+    raise EnvironmentError(
+OSError: /mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step12000 does not appear to have a file named config.json. Checkout 'https://huggingface.co//mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step12000/tree/main' for available files.

lm-evaluation-harness/wandb/run-20240523_130407-wvnshpcy/files/wandb-metadata.json

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+    "heartbeatAt": "2024-05-23T13:04:07.993231",
+    "startedAt": "2024-05-23T13:04:07.485994",
+    "docker": null,
+    "cuda": null,
+    "args": [
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+        "pretrained=/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step12000",
+        "--tasks",
+        "hellaswag,arc_easy,openbookqa,winogrande,sst2,mrpc",
+        "--batch_size",
+        "auto",
+        "--wandb_args",
+        "project=bharatgpt,group=trial_expt_2"
+    ],
+    "state": "running",
+    "program": "-m lm_eval.__main__",
+    "codePathLocal": null,
+    "git": {
+        "remote": "https://github.com/EleutherAI/lm-evaluation-harness",
+        "commit": null
+    },
+    "email": null,
+    "root": "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness",
+    "host": "peacock-evaluation-worker-0",
+    "username": "root",
+    "executable": "/usr/bin/python3",
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lm-evaluation-harness/wandb/run-20240608_190333-82mnef5m/files/config.yaml

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+
+_wandb:
+  desc: null
+  value:
+    python_version: 3.10.12
+    cli_version: 0.17.1
+    framework: huggingface
+    huggingface_version: 4.36.2
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1717873413
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+      4: 3.10.12
+      5: 0.17.1
+      6: 4.36.2
+      8:
+      - 5
+      13: linux-x86_64
+task_configs:
+  desc: null
+  value:
+    arc_easy:
+      task: arc_easy
+      group:
+      - ai2_arc
+      dataset_path: allenai/ai2_arc
+      dataset_name: ARC-Easy
+      training_split: train
+      validation_split: validation
+      test_split: test
+      doc_to_text: 'Question: {{question}}
+
+        Answer:'
+      doc_to_target: '{{choices.label.index(answerKey)}}'
+      doc_to_choice: '{{choices.text}}'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+        aggregation: mean
+        higher_is_better: true
+      - metric: acc_norm
+        aggregation: mean
+        higher_is_better: true
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: true
+      doc_to_decontamination_query: 'Question: {{question}}
+
+        Answer:'
+      metadata:
+        version: 1.0
+    boolq:
+      task: boolq
+      group:
+      - super-glue-lm-eval-v1
+      dataset_path: super_glue
+      dataset_name: boolq
+      training_split: train
+      validation_split: validation
+      doc_to_text: '{{passage}}
+
+        Question: {{question}}?
+
+        Answer:'
+      doc_to_target: label
+      doc_to_choice:
+      - 'no'
+      - 'yes'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: true
+      doc_to_decontamination_query: passage
+      metadata:
+        version: 2.0
+    copa:
+      task: copa
+      group:
+      - super-glue-lm-eval-v1
+      dataset_path: super_glue
+      dataset_name: copa
+      training_split: train
+      validation_split: validation
+      doc_to_text: "def doc_to_text(doc):\n    # Drop the period\n    connector =\
+        \ {\n        \"cause\": \"because\",\n        \"effect\": \"therefore\",\n\
+        \    }[doc[\"question\"]]\n    return doc[\"premise\"].strip()[:-1] + f\"\
+        \ {connector}\"\n"
+      doc_to_target: "def doc_to_target(doc):\n    correct_choice = doc[\"choice1\"\
+        ] if doc[\"label\"] == 0 else doc[\"choice2\"]\n    # Connect the sentences\n\
+        \    return \" \" + convert_choice(correct_choice)\n"
+      doc_to_choice: "def doc_to_choice(doc):\n    return [\" \" + convert_choice(doc[\"\
+        choice1\"]), \" \" + convert_choice(doc[\"choice2\"])]\n"
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: false
+      metadata:
+        version: 1.0
+    indic_arc_challenge_hi:
+      task: indic_arc_challenge_hi
+      group: Cognitive-Lab/Indic-ARC-Challenge
+      dataset_path: Cognitive-Lab/Indic-ARC-Challenge
+      dataset_name: hi
+      test_split: test
+      doc_to_text: 'Question: {{translated_question}}
+
+        Answer:'
+      doc_to_target: '{{translated_choices.label.index(answerKey)}}'
+      doc_to_choice: '{{translated_choices.text}}'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+        aggregation: mean
+        higher_is_better: true
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: true
+      doc_to_decontamination_query: 'Question: {{translated_question}}
+
+        Answer:'
+      metadata:
+        version: 1.0
+    indic_arc_easy_hi:
+      task: indic_arc_easy_hi
+      group: Cognitive-Lab/Indic-ARC-Easy
+      dataset_path: Cognitive-Lab/Indic-ARC-Easy
+      dataset_name: hi
+      test_split: test
+      doc_to_text: 'Question: {{translated_question}}
+
+        Answer:'
+      doc_to_target: '{{translated_choices.label.index(answerKey)}}'
+      doc_to_choice: '{{translated_choices.text}}'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+        aggregation: mean
+        higher_is_better: true
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: true
+      doc_to_decontamination_query: 'Question: {{translated_question}}
+
+        Answer:'
+      metadata:
+        version: 1.0
+    indic_boolq_hi:
+      task: indic_boolq_hi
+      group: Cognitive-Lab/Indic-BoolQ
+      dataset_path: Cognitive-Lab/Indic-BoolQ
+      dataset_name: hi
+      validation_split: validation
+      doc_to_text: 'Passage: {translated_passage}
+
+        Question: {translated_question.strip()}
+
+        Answer:'
+      doc_to_target: answer
+      doc_to_choice:
+      - 'true'
+      - 'false'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+        aggregation: mean
+        higher_is_better: true
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: false
+      metadata:
+        version: 1.0
+    mrpc:
+      task: mrpc
+      group: glue
+      dataset_path: glue
+      dataset_name: mrpc
+      training_split: train
+      validation_split: validation
+      doc_to_text: 'Sentence 1: {{sentence1}}
+
+        Sentence 2: {{sentence2}}
+
+        Question: Do both sentences mean the same thing?
+
+        Answer:'
+      doc_to_target: label
+      doc_to_choice:
+      - 'no'
+      - 'yes'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+      - metric: f1
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: false
+      metadata:
+        version: 1.0
+    piqa:
+      task: piqa
+      dataset_path: piqa
+      training_split: train
+      validation_split: validation
+      doc_to_text: 'Question: {{goal}}
+
+        Answer:'
+      doc_to_target: label
+      doc_to_choice: '{{[sol1, sol2]}}'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+        aggregation: mean
+        higher_is_better: true
+      - metric: acc_norm
+        aggregation: mean
+        higher_is_better: true
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: true
+      doc_to_decontamination_query: goal
+      metadata:
+        version: 1.0
+    sst2:
+      task: sst2
+      group: glue
+      dataset_path: glue
+      dataset_name: sst2
+      training_split: train
+      validation_split: validation
+      doc_to_text: '{{sentence}}
+
+        Question: Is this sentence positive or negative?
+
+        Answer:'
+      doc_to_target: label
+      doc_to_choice:
+      - negative
+      - positive
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: false
+      metadata:
+        version: 1.0
+    winogrande:
+      task: winogrande
+      dataset_path: winogrande
+      dataset_name: winogrande_xl
+      training_split: train
+      validation_split: validation
+      doc_to_text: "def doc_to_text(doc):\n    answer_to_num = {\"1\": 0, \"2\": 1}\n\
+        \    return answer_to_num[doc[\"answer\"]]\n"
+      doc_to_target: "def doc_to_target(doc):\n    idx = doc[\"sentence\"].index(\"\
+        _\") + 1\n    return doc[\"sentence\"][idx:].strip()\n"
+      doc_to_choice: "def doc_to_choice(doc):\n    idx = doc[\"sentence\"].index(\"\
+        _\")\n    options = [doc[\"option1\"], doc[\"option2\"]]\n    return [doc[\"\
+        sentence\"][:idx] + opt for opt in options]\n"
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+        aggregation: mean
+        higher_is_better: true
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: true
+      doc_to_decontamination_query: sentence
+      metadata:
+        version: 1.0
+cli_configs:
+  desc: null
+  value:
+    model: hf
+    model_args: pretrained=/mnt/weka/peacock/experiments/llama/eval/checkpoint-enhibn-updated/llamav2-3b/hf/global_step240000,tokenizer=/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer
+    batch_size: auto
+    batch_sizes:
+    - 64
+    device: null
+    use_cache: null
+    limit: null
+    bootstrap_iters: 100000
+    gen_kwargs: null

lm-evaluation-harness/wandb/run-20240608_190333-82mnef5m/files/media/table/evaluation/eval_results_1_fd1718bec4834f9c9150.table.json

@@ -0,0 +1 @@
+{"columns": ["Tasks", "Version", "Filter", "num_fewshot", "Metric", "Value", "Stderr"], "data": [["winogrande", 1.0, "none", 0, "acc", "0.5035516969218626", "0.0141"], ["sst2", 1.0, "none", 0, "acc", "0.518348623853211", "0.0169"], ["piqa", 1.0, "none", 0, "acc", "0.5174102285092492", "0.0117"], ["piqa", 1.0, "none", 0, "acc_norm", "0.5048966267682263", "0.0117"], ["mrpc", 1.0, "none", 0, "acc", "0.3161764705882353", "0.0230"], ["mrpc", 1.0, "none", 0, "f1", "0.0", "0.0000"], ["indic_boolq_hi", 1.0, "none", 0, "acc", "0.6217125382262997", "0.0085"], ["indic_arc_easy_hi", 1.0, "none", 0, "acc", "0.2398989898989899", "0.0088"], ["indic_arc_challenge_hi", 1.0, "none", 0, "acc", "0.20819112627986347", "0.0119"], ["copa", 1.0, "none", 0, "acc", "0.6", "0.0492"], ["boolq", 2.0, "none", 0, "acc", "0.3782874617737003", "0.0085"], ["arc_easy", 1.0, "none", 0, "acc", "0.26346801346801346", "0.0090"], ["arc_easy", 1.0, "none", 0, "acc_norm", "0.2647306397306397", "0.0091"]]}

lm-evaluation-harness/wandb/run-20240608_190333-82mnef5m/files/output.log

@@ -0,0 +1,744 @@
+
+2024-06-08:19:03:34,356 INFO     [__main__.py:251] Verbosity set to INFO
+2024-06-08:19:03:43,617 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'boolq', 'copa', 'indic_arc_challenge_hi', 'indic_arc_easy_hi', 'indic_boolq_hi', 'mrpc', 'piqa', 'sst2', 'winogrande']
+2024-06-08:19:03:43,618 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-06-08:19:03:43,618 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/eval/checkpoint-enhibn-updated/llamav2-3b/hf/global_step240000', 'tokenizer': '/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer'}
+2024-06-08:19:03:45,961 INFO     [huggingface.py:164] Using device 'cuda'
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
+  warnings.warn(
+Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+2024-06-08:19:04:20,128 WARNING  [task.py:763] [Task: boolq] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-06-08:19:04:20,129 WARNING  [task.py:775] [Task: boolq] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for super_glue contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/super_glue
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+2024-06-08:19:04:21,896 WARNING  [task.py:763] [Task: copa] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-06-08:19:04:21,897 WARNING  [task.py:775] [Task: copa] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+2024-06-08:19:04:25,087 WARNING  [task.py:322] [Task: indic_arc_challenge_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-06-08:19:04:25,088 WARNING  [task.py:322] [Task: indic_arc_challenge_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-06-08:19:04:26,859 WARNING  [task.py:322] [Task: indic_arc_easy_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-06-08:19:04:26,859 WARNING  [task.py:322] [Task: indic_arc_easy_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-06-08:19:04:28,760 WARNING  [task.py:763] [Task: mrpc] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-06-08:19:04:28,760 WARNING  [task.py:775] [Task: mrpc] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+2024-06-08:19:04:28,761 WARNING  [task.py:763] [Task: mrpc] metric f1 is defined, but aggregation is not. using default aggregation=f1
+2024-06-08:19:04:28,761 WARNING  [task.py:775] [Task: mrpc] metric f1 is defined, but higher_is_better is not. using default higher_is_better=True
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for piqa contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/piqa
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+2024-06-08:19:04:34,484 WARNING  [task.py:763] [Task: sst2] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-06-08:19:04:34,484 WARNING  [task.py:775] [Task: sst2] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for winogrande contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/winogrande
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+2024-06-08:19:04:42,011 INFO     [task.py:395] Building contexts for winogrande on rank 0...
+100%|██████████| 1267/1267 [00:00<00:00, 69176.18it/s]
+2024-06-08:19:04:42,102 INFO     [task.py:395] Building contexts for sst2 on rank 0...
+100%|██████████| 872/872 [00:00<00:00, 2597.98it/s]
+2024-06-08:19:04:42,466 INFO     [task.py:395] Building contexts for piqa on rank 0...
+100%|██████████| 1838/1838 [00:01<00:00, 1102.82it/s]
+2024-06-08:19:04:44,209 INFO     [task.py:395] Building contexts for mrpc on rank 0...
+100%|██████████| 408/408 [00:00<00:00, 1882.89it/s]
+2024-06-08:19:04:44,444 INFO     [task.py:395] Building contexts for indic_boolq_hi on rank 0...
+100%|██████████| 3270/3270 [00:01<00:00, 3060.92it/s]
+2024-06-08:19:04:45,684 INFO     [task.py:395] Building contexts for indic_arc_easy_hi on rank 0...
+100%|██████████| 2376/2376 [00:02<00:00, 1140.22it/s]
+2024-06-08:19:04:48,009 INFO     [task.py:395] Building contexts for indic_arc_challenge_hi on rank 0...
+100%|██████████| 1172/1172 [00:01<00:00, 1138.40it/s]
+2024-06-08:19:04:49,159 INFO     [task.py:395] Building contexts for copa on rank 0...
+100%|██████████| 100/100 [00:00<00:00, 62657.66it/s]
+2024-06-08:19:04:49,168 INFO     [task.py:395] Building contexts for boolq on rank 0...
+100%|██████████| 3270/3270 [00:01<00:00, 2005.31it/s]
+2024-06-08:19:04:50,927 INFO     [task.py:395] Building contexts for arc_easy on rank 0...
+100%|██████████| 2376/2376 [00:02<00:00, 1061.27it/s]
+2024-06-08:19:04:53,311 INFO     [evaluator.py:379] Running loglikelihood requests
+Token indices sequence length is longer than the specified maximum sequence length for this model (1333 > 1024). Running this sequence through the model will result in indexing errors
+Running loglikelihood requests:   0%|          | 0/45739 [00:00<?, ?it/s]
+Passed argument batch_size = auto:1. Detecting largest batch size
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+Running loglikelihood requests: 100%|██████████| 45739/45739 [1:07:44<00:00, 11.25it/s]
+  0%|          | 0/100 [00:00<?, ?it/s]
+
+
+100%|██████████| 100/100 [02:10<00:00,  1.30s/it]
+hf (pretrained=/mnt/weka/peacock/experiments/llama/eval/checkpoint-enhibn-updated/llamav2-3b/hf/global_step240000,tokenizer=/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto (64)
+|        Tasks         |Version|Filter|n-shot| Metric |Value |   |Stderr|
+|----------------------|------:|------|-----:|--------|-----:|---|-----:|
+|winogrande            |      1|none  |     0|acc     |0.5036|±  |0.0141|
+|sst2                  |      1|none  |     0|acc     |0.5183|±  |0.0169|
+|piqa                  |      1|none  |     0|acc     |0.5174|±  |0.0117|
+|                      |       |none  |     0|acc_norm|0.5049|±  |0.0117|
+|mrpc                  |      1|none  |     0|acc     |0.3162|±  |0.0230|
+|                      |       |none  |     0|f1      |0.0000|±  |0.0000|
+|indic_boolq_hi        |      1|none  |     0|acc     |0.6217|±  |0.0085|
+|indic_arc_easy_hi     |      1|none  |     0|acc     |0.2399|±  |0.0088|
+|indic_arc_challenge_hi|      1|none  |     0|acc     |0.2082|±  |0.0119|
+|copa                  |      1|none  |     0|acc     |0.6000|±  |0.0492|
+|boolq                 |      2|none  |     0|acc     |0.3783|±  |0.0085|
+|arc_easy              |      1|none  |     0|acc     |0.2635|±  |0.0090|
+|                      |       |none  |     0|acc_norm|0.2647|±  |0.0091|

lm-evaluation-harness/wandb/run-20240608_190333-82mnef5m/files/requirements.txt

@@ -0,0 +1,154 @@
+DataProperty==1.0.1
+GitPython==3.1.43
+Jinja2==3.1.4
+Markdown==3.6
+MarkupSafe==2.1.5
+Pillow-SIMD==7.0.0.post3
+PyYAML==6.0
+Werkzeug==3.0.3
+absl-py==2.1.0
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+colorama==0.4.6
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doc["choice2"]\n    # Connect the sentences\n    return " " + convert_choice(correct_choice)\n', 'doc_to_choice': 'def doc_to_choice(doc):\n    return [" " + convert_choice(doc["choice1"]), " " + convert_choice(doc["choice2"])]\n', 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc'}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': False, 'metadata': {'version': 1.0}}, 'indic_arc_challenge_hi': {'task': 'indic_arc_challenge_hi', 'group': 'Cognitive-Lab/Indic-ARC-Challenge', 'dataset_path': 'Cognitive-Lab/Indic-ARC-Challenge', 'dataset_name': 'hi', 'test_split': 'test', 'doc_to_text': 'Question: {{translated_question}}\nAnswer:', 'doc_to_target': '{{translated_choices.label.index(answerKey)}}', 'doc_to_choice': '{{translated_choices.text}}', 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc', 'aggregation': 'mean', 'higher_is_better': True}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': True, 'doc_to_decontamination_query': 'Question: {{translated_question}}\nAnswer:', 'metadata': {'version': 1.0}}, 'indic_arc_easy_hi': {'task': 'indic_arc_easy_hi', 'group': 'Cognitive-Lab/Indic-ARC-Easy', 'dataset_path': 'Cognitive-Lab/Indic-ARC-Easy', 'dataset_name': 'hi', 'test_split': 'test', 'doc_to_text': 'Question: {{translated_question}}\nAnswer:', 'doc_to_target': '{{translated_choices.label.index(answerKey)}}', 'doc_to_choice': '{{translated_choices.text}}', 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc', 'aggregation': 'mean', 'higher_is_better': True}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': True, 'doc_to_decontamination_query': 'Question: {{translated_question}}\nAnswer:', 'metadata': {'version': 1.0}}, 'indic_boolq_hi': {'task': 'indic_boolq_hi', 'group': 'Cognitive-Lab/Indic-BoolQ', 'dataset_path': 'Cognitive-Lab/Indic-BoolQ', 'dataset_name': 'hi', 'validation_split': 'validation', 'doc_to_text': 'Passage: {translated_passage}\nQuestion: {translated_question.strip()}\nAnswer:', 'doc_to_target': 'answer', 'doc_to_choice': ['true', 'false'], 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc', 'aggregation': 'mean', 'higher_is_better': True}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': False, 'metadata': {'version': 1.0}}, 'mrpc': {'task': 'mrpc', 'group': 'glue', 'dataset_path': 'glue', 'dataset_name': 'mrpc', 'training_split': 'train', 'validation_split': 'validation', 'doc_to_text': 'Sentence 1: {{sentence1}}\nSentence 2: {{sentence2}}\nQuestion: Do both sentences mean the same thing?\nAnswer:', 'doc_to_target': 'label', 'doc_to_choice': ['no', 'yes'], 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc'}, {'metric': 'f1'}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': False, 'metadata': {'version': 1.0}}, 'piqa': {'task': 'piqa', 'dataset_path': 'piqa', 'training_split': 'train', 'validation_split': 'validation', 'doc_to_text': 'Question: {{goal}}\nAnswer:', 'doc_to_target': 'label', 'doc_to_choice': '{{[sol1, sol2]}}', 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc', 'aggregation': 'mean', 'higher_is_better': True}, {'metric': 'acc_norm', 'aggregation': 'mean', 'higher_is_better': True}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': True, 'doc_to_decontamination_query': 'goal', 'metadata': {'version': 1.0}}, 'sst2': {'task': 'sst2', 'group': 'glue', 'dataset_path': 'glue', 'dataset_name': 'sst2', 'training_split': 'train', 'validation_split': 'validation', 'doc_to_text': '{{sentence}}\nQuestion: Is this sentence positive or negative?\nAnswer:', 'doc_to_target': 'label', 'doc_to_choice': ['negative', 'positive'], 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc'}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': False, 'metadata': {'version': 1.0}}, 'winogrande': {'task': 'winogrande', 'dataset_path': 'winogrande', 'dataset_name': 'winogrande_xl', 'training_split': 'train', 'validation_split': 'validation', 'doc_to_text': 'def doc_to_text(doc):\n    answer_to_num = {"1": 0, "2": 1}\n    return answer_to_num[doc["answer"]]\n', 'doc_to_target': 'def doc_to_target(doc):\n    idx = doc["sentence"].index("_") + 1\n    return doc["sentence"][idx:].strip()\n', 'doc_to_choice': 'def doc_to_choice(doc):\n    idx = doc["sentence"].index("_")\n    options = [doc["option1"], doc["option2"]]\n    return [doc["sentence"][:idx] + opt for opt in options]\n', 'description': '', 'target_delimiter': ' ', 'fewshot_delimiter': '\n\n', 'num_fewshot': 0, 'metric_list': [{'metric': 'acc', 'aggregation': 'mean', 'higher_is_better': True}], 'output_type': 'multiple_choice', 'repeats': 1, 'should_decontaminate': True, 'doc_to_decontamination_query': 'sentence', 'metadata': {'version': 1.0}}}, 'cli_configs': {'model': 'hf', 'model_args': 'pretrained=/mnt/weka/peacock/experiments/llama/eval/checkpoint-enhibn-updated/llamav2-3b/hf/global_step240000,tokenizer=/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer', 'batch_size': 'auto', 'batch_sizes': [64], 'device': None, 'use_cache': None, 'limit': None, 'bootstrap_iters': 100000, 'gen_kwargs': None}}
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venv/lib/python3.10/site-packages/transformers/models/flava/__init__.py

@@ -0,0 +1,97 @@
+# Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
+
+
+_import_structure = {
+    "configuration_flava": [
+        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "FlavaConfig",
+        "FlavaImageCodebookConfig",
+        "FlavaImageConfig",
+        "FlavaMultimodalConfig",
+        "FlavaTextConfig",
+    ],
+}
+
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["feature_extraction_flava"] = ["FlavaFeatureExtractor"]
+    _import_structure["image_processing_flava"] = ["FlavaImageProcessor"]
+    _import_structure["processing_flava"] = ["FlavaProcessor"]
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_flava"] = [
+        "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "FlavaForPreTraining",
+        "FlavaImageCodebook",
+        "FlavaImageModel",
+        "FlavaModel",
+        "FlavaMultimodalModel",
+        "FlavaPreTrainedModel",
+        "FlavaTextModel",
+    ]
+
+if TYPE_CHECKING:
+    from .configuration_flava import (
+        FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        FlavaConfig,
+        FlavaImageCodebookConfig,
+        FlavaImageConfig,
+        FlavaMultimodalConfig,
+        FlavaTextConfig,
+    )
+
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .feature_extraction_flava import FlavaFeatureExtractor
+        from .image_processing_flava import FlavaImageProcessor
+        from .processing_flava import FlavaProcessor
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_flava import (
+            FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
+            FlavaForPreTraining,
+            FlavaImageCodebook,
+            FlavaImageModel,
+            FlavaModel,
+            FlavaMultimodalModel,
+            FlavaPreTrainedModel,
+            FlavaTextModel,
+        )
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

venv/lib/python3.10/site-packages/transformers/models/flava/configuration_flava.py

@@ -0,0 +1,764 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" FLAVA model configurations"""
+
+import os
+from typing import Any, Dict, Union
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+from ..deprecated._archive_maps import FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP  # noqa: F401, E402
+
+
+class FlavaImageConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`FlavaImageModel`]. It is used to instantiate an
+    FLAVA model according to the specified arguments, defining the model architecture.
+
+    Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
+    [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        patch_size (`int`, *optional*, defaults to 16):
+            The size (resolution) of each patch.
+        num_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        qkv_bias (`bool`, *optional*, defaults to `True`):
+            Whether to add a bias to the queries, keys and values.
+        mask_token (`bool`, *optional*, defaults to `True`):
+            Whether to use a mask token or not. Used in MIM (Masked Image Modeling) loss for FLAVA.
+        vocab_size (`int`, *optional*, defaults to 8192):
+            Vocabulary size of the [`FlavaImageCodebook`] used in conjunction with [`FlavaImageModel`] for MIM (Masked
+            Image Modeling) loss for FLAVA.
+
+    Example:
+
+    ```python
+    >>> from transformers import FlavaImageConfig, FlavaImageModel
+
+    >>> # Initializing a FlavaImageModel with  style configuration
+    >>> configuration = FlavaImageConfig()
+
+    >>> # Initializing a FlavaImageModel model (with random weights) from the style configuration
+    >>> model = FlavaImageModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "flava_image_model"
+
+    def __init__(
+        self,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        intermediate_size: int = 3072,
+        hidden_act: int = "gelu",
+        hidden_dropout_prob: float = 0.0,
+        attention_probs_dropout_prob: float = 0.0,
+        initializer_range: float = 0.02,
+        layer_norm_eps: float = 1e-12,
+        image_size: int = 224,
+        patch_size: int = 16,
+        num_channels: int = 3,
+        qkv_bias: bool = True,
+        mask_token: bool = True,
+        vocab_size: int = 8192,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.qkv_bias = qkv_bias
+        self.mask_token = mask_token
+        self.vocab_size = vocab_size
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # get the image config dict if we are loading from FlavaConfig
+        if config_dict.get("model_type") == "flava":
+            config_dict = config_dict["image_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class FlavaTextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`FlavaTextModel`]. It is used to instantiate an
+    FLAVA model according to the specified arguments, defining the model architecture.
+
+    Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
+    [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 30522):
+            Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`FlavaTextModel`].
+        type_vocab_size (`int`, *optional*, defaults to 2):
+            The vocabulary size of the `token_type_ids` passed when calling [`FlavaTextModel`]. Note that even though
+            text encoder allows `token_type_ids`'s value as 2, for text-only pretraining and fine-tuning, only 1 is
+            used similar to RoBERTa.
+        max_position_embeddings (`int`, *optional*, defaults to 512):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048). For VL, max_length passed to model is 77.
+        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
+            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
+            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
+            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
+            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
+            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        patch_size (`int`, *optional*, defaults to 16):
+            The size (resolution) of each patch.
+        num_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        qkv_bias (`bool`, *optional*, defaults to `True`):
+            Whether to add a bias to the queries, keys and values.
+
+    Example:
+
+    ```python
+    >>> from transformers import FlavaTextConfig, FlavaTextModel
+
+    >>> # Initializing a FlavaTextModel with  style configuration
+    >>> configuration = FlavaTextConfig()
+
+    >>> # Initializing a FlavaTextModel model (with random weights) from the style configuration
+    >>> model = FlavaTextModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "flava_text_model"
+
+    def __init__(
+        self,
+        vocab_size: int = 30522,
+        type_vocab_size: int = 2,
+        max_position_embeddings: int = 512,
+        position_embedding_type: str = "absolute",
+        hidden_size: int = 768,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        intermediate_size: int = 3072,
+        hidden_act: str = "gelu",
+        hidden_dropout_prob: float = 0.0,
+        attention_probs_dropout_prob: float = 0.0,
+        initializer_range: float = 0.02,
+        layer_norm_eps: float = 1e-12,
+        pad_token_id: int = 0,
+        qkv_bias: bool = True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.vocab_size = vocab_size
+        self.type_vocab_size = type_vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.position_embedding_type = position_embedding_type
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.qkv_bias = qkv_bias
+        self.pad_token_id = pad_token_id
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # get the text config dict if we are loading from FlavaConfig
+        if config_dict.get("model_type") == "flava":
+            config_dict = config_dict["text_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class FlavaMultimodalConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`FlavaMultimodalModel`]. It is used to instantiate
+    an FLAVA model according to the specified arguments, defining the model architecture.
+
+    Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
+    [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 6):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        qkv_bias (`bool`, *optional*, defaults to `True`):
+            Whether to add a bias to the queries, keys and values.
+        use_cls_token (`bool`, *optional*, defaults to `True`):
+            Whether to use an extra CLS token for multimodal settings. Usually needed by the FLAVA model.
+
+
+    Example:
+
+    ```python
+    >>> from transformers import FlavaMultimodalConfig, FlavaMultimodalModel
+
+    >>> # Initializing a FlavaMultimodalModel with  style configuration
+    >>> configuration = FlavaMultimodalConfig()
+
+    >>> # Initializing a FlavaMultimodalModel model (with random weights) from the style configuration
+    >>> model = FlavaMultimodalModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "flava_multimodal_model"
+
+    def __init__(
+        self,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 6,
+        num_attention_heads: int = 12,
+        intermediate_size: int = 3072,
+        hidden_act: int = "gelu",
+        hidden_dropout_prob: int = 0.0,
+        attention_probs_dropout_prob: int = 0.0,
+        initializer_range: float = 0.02,
+        layer_norm_eps: float = 1e-12,
+        qkv_bias: bool = True,
+        use_cls_token: bool = True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.qkv_bias = qkv_bias
+        self.use_cls_token = use_cls_token
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # get the multimodal config dict if we are loading from FlavaConfig
+        if config_dict.get("model_type") == "flava":
+            config_dict = config_dict["multimodal_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class FlavaImageCodebookConfig(PretrainedConfig):
+    model_type = "flava_image_codebook"
+
+    r"""
+    [`FlavaImageCodebookConfig`] is the configuration class to store the configuration of a [`FlavaImageCodebook`]. It
+    is used to instantiate an FLAVA model according to the specified arguments, defining the model architecture.
+    Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
+    [facebook/flava-image-codebook](https://huggingface.co/facebook/flava-image-codebook) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        num_groups (`int`, defaults to 4):
+            Number of groups to be created. This parameter as of now doesn't affect the model and is used for some
+            internal calculation and estimations.
+        input_channels (`int`, defaults to 3):
+            Number of channels in the image to be passed.
+        num_blocks_per_group (`int`, defaults to 2):
+            Number of conv-based blocks per group.
+        hidden_size (`int`, defaults to 256):
+            Size of hidden dim for the blocks.
+        vocab_size (`int`, defaults to 8192):
+            Size of the output vocabulary for the codebook.
+        freeze (`bool`, defaults to `True`):
+            Whether to freeze the weights of the model.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        kwargs (*optional*):
+            Dictionary of keyword arguments.
+
+    Example:
+
+    ```python
+    >>> from transformers import FlavaImageCodebookConfig, FlavaImageCodebook
+
+    >>> # Initializing a FlavaImageCodebook with style configuration
+    >>> configuration = FlavaImageCodebookConfig()
+
+    >>> # Initializing a FlavaImageCodebook model (with random weights) from the style configuration
+    >>> model = FlavaImageCodebook(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
+    """
+
+    def __init__(
+        self,
+        num_groups: int = 4,
+        input_channels: int = 3,
+        num_blocks_per_group: int = 2,
+        hidden_size: int = 256,
+        vocab_size: int = 8192,
+        freeze: int = True,
+        initializer_range: float = 0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.num_groups = num_groups
+        self.input_channels = input_channels
+        self.num_blocks_per_group = num_blocks_per_group
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab_size
+        self.freeze = freeze
+        self.initializer_range = initializer_range
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # get the image codebook config dict if we are loading from FlavaConfig
+        if config_dict.get("model_type") == "flava":
+            config_dict = config_dict["image_codebook_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class FlavaConfig(PretrainedConfig):
+    r"""
+    [`FlavaConfig`] is the configuration class to store the configuration of a [`FlavaModel`]. It is used to
+    instantiate FLAVA model according to the specified arguments, defining the text model, image model, image codebook
+    and multimodal model configs. Instantiating a configuration with the defaults will yield a similar configuration to
+    that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        text_config (`dict`, *optional*):
+            Dictionary of configuration options used to initialize [`FlavaTextConfig`].
+        image_config (`dict`, *optional*):
+            Dictionary of configuration options used to initialize [`FlavaImageConfig`].
+        multimodal_config (`dict`, *optional*):
+            Dictionary of configuration options used to initialize [`FlavaMultimodalConfig`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        projection_dim (`int`, *optional*, defaults to 512):
+            Dimentionality of text and image projection layers.
+        logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
+            The inital value of the *logit_scale* paramter. Default is used as per the original FLAVA/CLIP
+            implementation.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        ce_ignore_index (`int`, *optional*, defaults to -100):
+            Cross entropy index to ignore.
+        mim_weight (`float`, *optional*, defaults to 1.0):
+            Weight to be assigned to MIM (Masked Image Modeling) unimodal loss
+        mlm_weight (`float`, *optional*, defaults to 1.0):
+            Weight to be assigned to MLM (Masked Language Modeling) unimodal loss
+        global_contrastive_weight (`float`, *optional*, defaults to 1.0):
+            Weight to be assigned to global contrastive cross-alignment loss.
+        itm_weight (`float`, *optional*, defaults to 1.0):
+            Weight to be assigned to image-text matching multimodal loss.
+        mmm_image_weight (`float`, *optional*, defaults to 1.0):
+            Weight to be assigned to MMM loss's image part.
+        mmm_text_weight (`float`, *optional*, defaults to 1.0):
+            Weight to be assigned to MMM loss's text part.
+        global_backprop_contrastive (`bool`, *optional*, defaults to `True`):
+            Whether to use global backpropgation through all workers in contrastive loss.
+        skip_unmasked_multimodal_encoder (`bool`, *optional*, defaults to `True`):
+            Whether to skip running unmasked multimodal encoder whose outputs are not used by FLAVA losses.
+        return_loss (`bool`, *optional*, defaults to `True`):
+            Whether to return loss or not
+
+        kwargs (*optional*):
+            Dictionary of keyword arguments.
+
+    Example:
+
+    ```python
+    >>> from transformers import FlavaConfig, FlavaModel, FlavaForPreTraining
+
+    >>> # Initializing a FlavaConfig with style configuration
+    >>> configuration = FlavaConfig()
+
+    >>> # Initializing a FlavaModel and FlavaForPreTraining model (with random weights) from the style configuration
+    >>> model = FlavaModel(configuration)
+    >>> model_pre = FlavaForPreTraining(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    >>> configuration_pre = model_pre.config
+    ```
+    """
+
+    model_type = "flava"
+
+    def __init__(
+        self,
+        image_config: Dict[str, Any] = None,
+        text_config: Dict[str, Any] = None,
+        multimodal_config: Dict[str, Any] = None,
+        image_codebook_config: Dict[str, Any] = None,
+        hidden_size: int = 768,
+        layer_norm_eps: float = 1e-12,
+        projection_dim: int = 768,
+        init_codebook: bool = True,
+        logit_scale_init_value: float = 2.6592,
+        initializer_range: float = 0.02,
+        ce_ignore_index: int = -100,
+        mim_weight: float = 1.0,
+        mlm_weight: float = 1.0,
+        global_contrastive_weight: float = 1.0,
+        itm_weight: float = 1.0,
+        mmm_image_weight: float = 1.0,
+        mmm_text_weight: float = 1.0,
+        global_backprop_contrastive: bool = True,
+        skip_unmasked_multimodal_encoder: bool = True,
+        return_loss: bool = True,
+        **kwargs,
+    ):
+        # If `_config_dict` exist, we use them for the backward compatibility.
+        # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
+        # of confusion!).
+        text_config_dict = kwargs.pop("text_config_dict", None)
+        image_config_dict = kwargs.pop("image_config_dict", None)
+        multimodal_config_dict = kwargs.pop("multimodal_config_dict", None)
+        image_codebook_config_dict = kwargs.pop("image_codebook_config_dict", None)
+
+        super().__init__(**kwargs)
+
+        # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
+        # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
+        # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
+        if text_config_dict is not None:
+            if text_config is None:
+                text_config = {}
+
+            # This is the complete result when using `text_config_dict`.
+            _text_config_dict = FlavaTextConfig(**text_config_dict).to_dict()
+
+            # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
+            for key, value in _text_config_dict.items():
+                if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
+                    # If specified in `text_config_dict`
+                    if key in text_config_dict:
+                        message = (
+                            f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
+                            f'The value `text_config_dict["{key}"]` will be used instead.'
+                        )
+                    # If inferred from default argument values (just to be super careful)
+                    else:
+                        message = (
+                            f"`text_config_dict` is provided which will be used to initialize `FlavaTextConfig`. The "
+                            f'value `text_config["{key}"]` will be overriden.'
+                        )
+                    logger.info(message)
+
+            # Update all values in `text_config` with the ones in `_text_config_dict`.
+            text_config.update(_text_config_dict)
+
+        if image_config_dict is not None:
+            if image_config is None:
+                image_config = {}
+
+            # This is the complete result when using `image_config_dict`.
+            _image_config_dict = FlavaImageConfig(**image_config_dict).to_dict()
+            # convert keys to string instead of integer
+            if "id2label" in _image_config_dict:
+                _image_config_dict["id2label"] = {
+                    str(key): value for key, value in _image_config_dict["id2label"].items()
+                }
+
+            # Give a warning if the values exist in both `_image_config_dict` and `image_config` but being different.
+            for key, value in _image_config_dict.items():
+                if key in image_config and value != image_config[key] and key not in ["transformers_version"]:
+                    # If specified in `image_config_dict`
+                    if key in image_config_dict:
+                        message = (
+                            f"`{key}` is found in both `image_config_dict` and `image_config` but with different "
+                            f'values. The value `image_config_dict["{key}"]` will be used instead.'
+                        )
+                    # If inferred from default argument values (just to be super careful)
+                    else:
+                        message = (
+                            f"`image_config_dict` is provided which will be used to initialize `FlavaImageConfig`. "
+                            f'The value `image_config["{key}"]` will be overriden.'
+                        )
+                    logger.info(message)
+
+            # Update all values in `image_config` with the ones in `_image_config_dict`.
+            image_config.update(_image_config_dict)
+
+        if multimodal_config_dict is not None:
+            if multimodal_config is None:
+                multimodal_config = {}
+
+            # This is the complete result when using `multimodal_config_dict`.
+            _multimodal_config_dict = FlavaMultimodalConfig(**multimodal_config_dict).to_dict()
+
+            # Give a warning if the values exist in both `_multimodal_config_dict` and `multimodal_config` but being
+            # different.
+            for key, value in _multimodal_config_dict.items():
+                if (
+                    key in multimodal_config
+                    and value != multimodal_config[key]
+                    and key not in ["transformers_version"]
+                ):
+                    # If specified in `multimodal_config_dict`
+                    if key in multimodal_config_dict:
+                        message = (
+                            f"`{key}` is found in both `multimodal_config_dict` and `multimodal_config` but with "
+                            f'different values. The value `multimodal_config_dict["{key}"]` will be used instead.'
+                        )
+                    # If inferred from default argument values (just to be super careful)
+                    else:
+                        message = (
+                            f"`multimodal_config_dict` is provided which will be used to initialize "
+                            f'`FlavaMultimodalConfig`. The value `multimodal_config["{key}"]` will be overriden.'
+                        )
+                    logger.info(message)
+
+            # Update all values in `multimodal_config` with the ones in `_multimodal_config_dict`.
+            multimodal_config.update(_multimodal_config_dict)
+
+        if image_codebook_config_dict is not None:
+            if image_codebook_config is None:
+                image_codebook_config = {}
+
+            # This is the complete result when using `image_codebook_config_dict`.
+            _image_codebook_config_dict = FlavaImageCodebookConfig(**image_codebook_config_dict).to_dict()
+
+            # Give a warning if the values exist in both `_image_codebook_config_dict` and `image_codebook_config` but
+            # being different.
+            for key, value in _image_codebook_config_dict.items():
+                if (
+                    key in image_codebook_config
+                    and value != image_codebook_config[key]
+                    and key not in ["transformers_version"]
+                ):
+                    # If specified in `image_codebook_config_dict`
+                    if key in image_codebook_config_dict:
+                        message = (
+                            f"`{key}` is found in both `image_codebook_config_dict` and `image_codebook_config` but "
+                            f'with different values. The value `image_codebook_config_dict["{key}"]` will be used '
+                            "instead."
+                        )
+                    # If inferred from default argument values (just to be super careful)
+                    else:
+                        message = (
+                            f"`image_codebook_config_dict` is provided which will be used to initialize "
+                            f'`FlavaImageCodebookConfig`. The value `image_codebook_config["{key}"]` will be overriden.'
+                        )
+                    logger.info(message)
+
+            # Update all values in `image_codebook_config` with the ones in `_image_codebook_config_dict`.
+            image_codebook_config.update(_image_codebook_config_dict)
+
+        if image_config is None:
+            image_config = {}
+            logger.info("`image_config` is `None`. initializing the `FlavaImageConfig` with default values.")
+
+        if text_config is None:
+            text_config = {}
+            logger.info("`text_config` is `None`. Initializing the `FlavaTextConfig` with default values.")
+
+        if multimodal_config is None:
+            multimodal_config = {}
+            logger.info("`multimodal_config` is `None`. initializing the `FlavaMultimodalConfig` with default values.")
+
+        if image_codebook_config is None:
+            image_codebook_config = {}
+            logger.info(
+                "`image_codebook_config` is `None`. initializing the `FlavaImageCodebookConfig` with default values."
+            )
+
+        self.image_config = FlavaImageConfig(**image_config)
+        self.text_config = FlavaTextConfig(**text_config)
+        self.multimodal_config = FlavaMultimodalConfig(**multimodal_config)
+        self.image_codebook_config = FlavaImageCodebookConfig(**image_codebook_config)
+        self.projection_dim = projection_dim
+        self.init_codebook = init_codebook
+
+        self.hidden_size = hidden_size
+        self.layer_norm_eps = layer_norm_eps
+        self.initializer_range = initializer_range
+        self.logit_scale_init_value = logit_scale_init_value
+        self.initializer_factor = 1.0
+        self.ce_ignore_index = ce_ignore_index
+        self.mim_weight = mim_weight
+        self.mlm_weight = mlm_weight
+        self.global_contrastive_weight = global_contrastive_weight
+        self.itm_weight = itm_weight
+        self.mmm_image_weight = mmm_image_weight
+        self.mmm_text_weight = mmm_text_weight
+        self.global_backprop_contrastive = global_backprop_contrastive
+        self.skip_unmasked_multimodal_encoder = skip_unmasked_multimodal_encoder
+        self.return_loss = return_loss
+
+    @classmethod
+    def from_configs(
+        cls,
+        image_config: FlavaImageConfig,
+        text_config: FlavaTextConfig,
+        multimodal_config: FlavaMultimodalConfig,
+        image_codebook_config: FlavaImageCodebookConfig,
+        **kwargs,
+    ):
+        r"""
+        Instantiate a [`FlavaConfig`] (or a derived class) from flava text model configuration, flava image model
+        configuration, flava multimodal model and flava codebook model configuration.
+
+        Returns:
+            [`FlavaConfig`]: An instance of a configuration object
+        """
+
+        return cls(
+            image_config=image_config.to_dict(),
+            text_config=text_config.to_dict(),
+            multimodal_config=multimodal_config.to_dict(),
+            image_codebook_config=image_codebook_config.to_dict(),
+            **kwargs,
+        )

venv/lib/python3.10/site-packages/transformers/models/flava/convert_dalle_to_flava_codebook.py

@@ -0,0 +1,102 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import os
+
+import torch
+
+from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
+
+
+def rreplace(s, old, new, occurrence):
+    li = s.rsplit(old, occurrence)
+    return new.join(li)
+
+
+def count_parameters(state_dict):
+    # encoder.embeddings are double copied in original FLAVA
+    return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items())
+
+
+def upgrade_state_dict(state_dict):
+    upgrade = {}
+
+    group_keys = ["group_1", "group_2", "group_3", "group_4"]
+    for key, value in state_dict.items():
+        for group_key in group_keys:
+            if group_key in key:
+                key = key.replace(f"{group_key}.", f"{group_key}.group.")
+
+        if "res_path" in key:
+            key = key.replace("res_path.", "res_path.path.")
+
+        if key.endswith(".w"):
+            key = rreplace(key, ".w", ".weight", 1)
+        if key.endswith(".b"):
+            key = rreplace(key, ".b", ".bias", 1)
+
+        upgrade[key] = value.float()
+
+    return upgrade
+
+
+@torch.no_grad()
+def convert_dalle_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path=None, save_checkpoint=True):
+    """
+    Copy/paste/tweak model's weights to transformers design.
+    """
+    from dall_e import Encoder
+
+    encoder = Encoder()
+    if os.path.exists(checkpoint_path):
+        ckpt = torch.load(checkpoint_path)
+    else:
+        ckpt = torch.hub.load_state_dict_from_url(checkpoint_path)
+
+    if isinstance(ckpt, Encoder):
+        ckpt = ckpt.state_dict()
+    encoder.load_state_dict(ckpt)
+
+    if config_path is not None:
+        config = FlavaImageCodebookConfig.from_pretrained(config_path)
+    else:
+        config = FlavaImageCodebookConfig()
+
+    hf_model = FlavaImageCodebook(config).eval()
+    state_dict = encoder.state_dict()
+
+    hf_state_dict = upgrade_state_dict(state_dict)
+    hf_model.load_state_dict(hf_state_dict)
+    hf_state_dict = hf_model.state_dict()
+    hf_count = count_parameters(hf_state_dict)
+    state_dict_count = count_parameters(state_dict)
+
+    assert torch.allclose(hf_count, state_dict_count, atol=1e-3)
+
+    if save_checkpoint:
+        hf_model.save_pretrained(pytorch_dump_folder_path)
+    else:
+        return hf_state_dict
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
+    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
+    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
+    args = parser.parse_args()
+
+    convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

venv/lib/python3.10/site-packages/transformers/models/flava/convert_flava_original_pytorch_to_hf.py

@@ -0,0 +1,99 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import os
+
+import torch
+
+from transformers import FlavaConfig, FlavaForPreTraining
+from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
+
+
+def count_parameters(state_dict):
+    # encoder.embeddings are double copied in original FLAVA
+    return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items())
+
+
+def upgrade_state_dict(state_dict, codebook_state_dict):
+    upgrade = {}
+
+    for key, value in state_dict.items():
+        if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
+            continue
+
+        key = key.replace("heads.cmd.mim_head.cls.predictions", "mmm_image_head")
+        key = key.replace("heads.cmd.mlm_head.cls.predictions", "mmm_text_head")
+        key = key.replace("heads.cmd.itm_head.cls", "itm_head")
+        key = key.replace("heads.cmd.itm_head.pooler", "itm_head.pooler")
+        key = key.replace("heads.cmd.clip_head.logit_scale", "flava.logit_scale")
+        key = key.replace("heads.fairseq_mlm.cls.predictions", "mlm_head")
+        key = key.replace("heads.imagenet.mim_head.cls.predictions", "mim_head")
+        key = key.replace("mm_text_projection", "flava.text_to_mm_projection")
+        key = key.replace("mm_image_projection", "flava.image_to_mm_projection")
+        key = key.replace("image_encoder.module", "flava.image_model")
+        key = key.replace("text_encoder.module", "flava.text_model")
+        key = key.replace("mm_encoder.module.encoder.cls_token", "flava.multimodal_model.cls_token")
+        key = key.replace("mm_encoder.module", "flava.multimodal_model")
+        key = key.replace("text_projection", "flava.text_projection")
+        key = key.replace("image_projection", "flava.image_projection")
+
+        upgrade[key] = value.float()
+
+    for key, value in codebook_state_dict.items():
+        upgrade[f"image_codebook.{key}"] = value
+
+    return upgrade
+
+
+@torch.no_grad()
+def convert_flava_checkpoint(checkpoint_path, codebook_path, pytorch_dump_folder_path, config_path=None):
+    """
+    Copy/paste/tweak model's weights to transformers design.
+    """
+    if config_path is not None:
+        config = FlavaConfig.from_pretrained(config_path)
+    else:
+        config = FlavaConfig()
+
+    hf_model = FlavaForPreTraining(config).eval()
+
+    codebook_state_dict = convert_dalle_checkpoint(codebook_path, None, save_checkpoint=False)
+
+    if os.path.exists(checkpoint_path):
+        state_dict = torch.load(checkpoint_path, map_location="cpu")
+    else:
+        state_dict = torch.hub.load_state_dict_from_url(checkpoint_path, map_location="cpu")
+
+    hf_state_dict = upgrade_state_dict(state_dict, codebook_state_dict)
+    hf_model.load_state_dict(hf_state_dict)
+    hf_state_dict = hf_model.state_dict()
+    hf_count = count_parameters(hf_state_dict)
+    state_dict_count = count_parameters(state_dict) + count_parameters(codebook_state_dict)
+
+    assert torch.allclose(hf_count, state_dict_count, atol=1e-3)
+
+    hf_model.save_pretrained(pytorch_dump_folder_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
+    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
+    parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
+    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
+    args = parser.parse_args()
+
+    convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)

venv/lib/python3.10/site-packages/transformers/models/flava/feature_extraction_flava.py

@@ -0,0 +1,33 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Feature extractor class for FLAVA."""
+
+import warnings
+
+from ...utils import logging
+from .image_processing_flava import FlavaImageProcessor
+
+
+logger = logging.get_logger(__name__)
+
+
+class FlavaFeatureExtractor(FlavaImageProcessor):
+    def __init__(self, *args, **kwargs) -> None:
+        warnings.warn(
+            "The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
+            " use FlavaImageProcessor instead.",
+            FutureWarning,
+        )
+        super().__init__(*args, **kwargs)

venv/lib/python3.10/site-packages/transformers/models/flava/image_processing_flava.py

@@ -0,0 +1,738 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for Flava."""
+
+import math
+import random
+from functools import lru_cache
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import resize, to_channel_dimension_format
+from ...image_utils import (
+    OPENAI_CLIP_MEAN,
+    OPENAI_CLIP_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    infer_channel_dimension_format,
+    is_scaled_image,
+    make_list_of_images,
+    to_numpy_array,
+    valid_images,
+    validate_kwargs,
+    validate_preprocess_arguments,
+)
+from ...utils import TensorType, is_vision_available, logging
+
+
+if is_vision_available():
+    import PIL
+
+
+logger = logging.get_logger(__name__)
+
+
+# These values are taken from CLIP
+FLAVA_IMAGE_MEAN = OPENAI_CLIP_MEAN
+FLAVA_IMAGE_STD = OPENAI_CLIP_STD
+FLAVA_CODEBOOK_MEAN = [0.0, 0.0, 0.0]
+FLAVA_CODEBOOK_STD = [1.0, 1.0, 1.0]
+LOGIT_LAPLACE_EPS: float = 0.1
+
+
+# Inspired from https://github.com/microsoft/unilm/blob/master/beit/masking_generator.py
+class FlavaMaskingGenerator:
+    def __init__(
+        self,
+        input_size: Union[int, Tuple[int, int]] = 14,
+        total_mask_patches: int = 75,
+        mask_group_max_patches: Optional[int] = None,
+        mask_group_min_patches: int = 16,
+        mask_group_min_aspect_ratio: Optional[float] = 0.3,
+        mask_group_max_aspect_ratio: float = None,
+    ):
+        if not isinstance(input_size, tuple):
+            input_size = (input_size,) * 2
+        self.height, self.width = input_size
+
+        self.num_patches = self.height * self.width
+        self.total_mask_patches = total_mask_patches
+
+        self.mask_group_min_patches = mask_group_min_patches
+        self.mask_group_max_patches = total_mask_patches if mask_group_max_patches is None else mask_group_max_patches
+
+        mask_group_max_aspect_ratio = mask_group_max_aspect_ratio or 1 / mask_group_min_aspect_ratio
+        self.log_aspect_ratio = (math.log(mask_group_min_aspect_ratio), math.log(mask_group_max_aspect_ratio))
+
+    def __repr__(self):
+        repr_str = "MaskingGenerator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % (
+            self.height,
+            self.width,
+            self.mask_group_min_patches,
+            self.mask_group_max_patches,
+            self.total_mask_patches,
+            self.log_aspect_ratio[0],
+            self.log_aspect_ratio[1],
+        )
+        return repr_str
+
+    def get_shape(self):
+        return self.height, self.width
+
+    def _mask(self, mask, max_mask_patches):
+        delta = 0
+        for _attempt in range(10):
+            target_area = random.uniform(self.mask_group_min_patches, max_mask_patches)
+            aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
+            height = int(round(math.sqrt(target_area * aspect_ratio)))
+            width = int(round(math.sqrt(target_area / aspect_ratio)))
+            if width < self.width and height < self.height:
+                top = random.randint(0, self.height - height)
+                left = random.randint(0, self.width - width)
+
+                num_masked = mask[top : top + height, left : left + width].sum()
+                # Overlap
+                if 0 < height * width - num_masked <= max_mask_patches:
+                    for i in range(top, top + height):
+                        for j in range(left, left + width):
+                            if mask[i, j] == 0:
+                                mask[i, j] = 1
+                                delta += 1
+
+                if delta > 0:
+                    break
+        return delta
+
+    def __call__(self):
+        mask = np.zeros(shape=self.get_shape(), dtype=int)
+        mask_count = 0
+        while mask_count < self.total_mask_patches:
+            max_mask_patches = self.total_mask_patches - mask_count
+            max_mask_patches = min(max_mask_patches, self.mask_group_max_patches)
+
+            delta = self._mask(mask, max_mask_patches)
+            if delta == 0:
+                break
+            else:
+                mask_count += delta
+
+        return mask
+
+
+class FlavaImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a Flava image processor.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
+            `do_resize` parameter in `preprocess`.
+        size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
+            Size of the image after resizing. Can be overridden by the `size` parameter in `preprocess`.
+        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in
+            `preprocess`.
+        do_center_crop (`bool`, *optional*, defaults to `True`):
+            Whether to center crop the images. Can be overridden by the `do_center_crop` parameter in `preprocess`.
+        crop_size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
+            Size of image after the center crop `(crop_size["height"], crop_size["width"])`. Can be overridden by the
+            `crop_size` parameter in `preprocess`.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+            parameter in `preprocess`.
+        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+            Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in
+            `preprocess`.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in `preprocess`.
+        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
+            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+        return_image_mask (`bool`, *optional*, defaults to `False`):
+            Whether to return the image mask. Can be overridden by the `return_image_mask` parameter in `preprocess`.
+        input_size_patches (`int`, *optional*, defaults to 14):
+            Number of patches in the image in height and width direction. 14x14 = 196 total patches. Can be overridden
+            by the `input_size_patches` parameter in `preprocess`.
+        total_mask_patches (`int`, *optional*, defaults to 75):
+            Total number of patches that should be masked. Can be overridden by the `total_mask_patches` parameter in
+            `preprocess`.
+        mask_group_min_patches (`int`, *optional*, defaults to 16):
+            Minimum number of patches that should be masked. Can be overridden by the `mask_group_min_patches`
+            parameter in `preprocess`.
+        mask_group_max_patches (`int`, *optional*):
+            Maximum number of patches that should be masked. Can be overridden by the `mask_group_max_patches`
+            parameter in `preprocess`.
+        mask_group_min_aspect_ratio (`float`, *optional*, defaults to 0.3):
+            Minimum aspect ratio of the mask window. Can be overridden by the `mask_group_min_aspect_ratio` parameter
+            in `preprocess`.
+        mask_group_max_aspect_ratio (`float`, *optional*):
+            Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter
+            in `preprocess`.
+        codebook_do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the input for codebook to a certain. Can be overridden by the `codebook_do_resize`
+            parameter in `preprocess`. `codebook_size`.
+        codebook_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
+            Resize the input for codebook to the given size. Can be overridden by the `codebook_size` parameter in
+            `preprocess`.
+        codebook_resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.LANCZOS`):
+            Resampling filter to use if resizing the codebook image. Can be overridden by the `codebook_resample`
+            parameter in `preprocess`.
+        codebook_do_center_crop (`bool`, *optional*, defaults to `True`):
+            Whether to crop the input for codebook at the center. If the input size is smaller than
+            `codebook_crop_size` along any edge, the image is padded with 0's and then center cropped. Can be
+            overridden by the `codebook_do_center_crop` parameter in `preprocess`.
+        codebook_crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
+            Desired output size for codebook input when applying center-cropping. Can be overridden by the
+            `codebook_crop_size` parameter in `preprocess`.
+        codebook_do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the input for codebook by the specified scale `codebook_rescale_factor`. Can be
+            overridden by the `codebook_do_rescale` parameter in `preprocess`.
+        codebook_rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+            Defines the scale factor to use if rescaling the codebook image. Can be overridden by the
+            `codebook_rescale_factor` parameter in `preprocess`.
+        codebook_do_map_pixels (`bool`, *optional*, defaults to `True`):
+            Whether to map the pixel values of the codebook input to (1 - 2e)x + e. Can be overridden by the
+            `codebook_do_map_pixels` parameter in `preprocess`.
+        codebook_do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether or not to normalize the input for codebook with `codebook_image_mean` and `codebook_image_std`. Can
+            be overridden by the `codebook_do_normalize` parameter in `preprocess`.
+        codebook_image_mean (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0, 0, 0]`):
+            The sequence of means for each channel, to be used when normalizing images for codebook. Can be overridden
+            by the `codebook_image_mean` parameter in `preprocess`.
+        codebook_image_std (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
+            The sequence of standard deviations for each channel, to be used when normalizing images for codebook. Can
+            be overridden by the `codebook_image_std` parameter in `preprocess`.
+    """
+
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        do_center_crop: bool = True,
+        crop_size: Dict[str, int] = None,
+        do_rescale: bool = True,
+        rescale_factor: Union[int, float] = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: Optional[Union[float, Iterable[float]]] = None,
+        image_std: Optional[Union[float, Iterable[float]]] = None,
+        # Mask related params
+        return_image_mask: bool = False,
+        input_size_patches: int = 14,
+        total_mask_patches: int = 75,
+        mask_group_min_patches: int = 16,
+        mask_group_max_patches: Optional[int] = None,
+        mask_group_min_aspect_ratio: float = 0.3,
+        mask_group_max_aspect_ratio: Optional[float] = None,
+        # Codebook related params
+        return_codebook_pixels: bool = False,
+        codebook_do_resize: bool = True,
+        codebook_size: bool = None,
+        codebook_resample: int = PILImageResampling.LANCZOS,
+        codebook_do_center_crop: bool = True,
+        codebook_crop_size: int = None,
+        codebook_do_rescale: bool = True,
+        codebook_rescale_factor: Union[int, float] = 1 / 255,
+        codebook_do_map_pixels: bool = True,
+        codebook_do_normalize: bool = True,
+        codebook_image_mean: Optional[Union[float, Iterable[float]]] = None,
+        codebook_image_std: Optional[Union[float, Iterable[float]]] = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        size = size if size is not None else {"height": 224, "width": 224}
+        size = get_size_dict(size)
+        crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
+
+        codebook_size = codebook_size if codebook_size is not None else {"height": 112, "width": 112}
+        codebook_size = get_size_dict(codebook_size, param_name="codebook_size")
+        codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}
+        codebook_crop_size = get_size_dict(codebook_crop_size, param_name="codebook_crop_size")
+
+        self.do_resize = do_resize
+        self.size = size
+        self.resample = resample
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_center_crop = do_center_crop
+        self.crop_size = crop_size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean if image_mean is not None else FLAVA_IMAGE_MEAN
+        self.image_std = image_std if image_std is not None else FLAVA_IMAGE_STD
+
+        self.return_image_mask = return_image_mask
+        self.input_size_patches = input_size_patches
+        self.total_mask_patches = total_mask_patches
+        self.mask_group_min_patches = mask_group_min_patches
+        self.mask_group_max_patches = mask_group_max_patches
+        self.mask_group_min_aspect_ratio = mask_group_min_aspect_ratio
+        self.mask_group_max_aspect_ratio = mask_group_max_aspect_ratio
+
+        self.return_codebook_pixels = return_codebook_pixels
+        self.codebook_do_resize = codebook_do_resize
+        self.codebook_size = codebook_size
+        self.codebook_resample = codebook_resample
+        self.codebook_do_center_crop = codebook_do_center_crop
+        self.codebook_crop_size = codebook_crop_size
+        self.codebook_do_rescale = codebook_do_rescale
+        self.codebook_rescale_factor = codebook_rescale_factor
+        self.codebook_do_map_pixels = codebook_do_map_pixels
+        self.codebook_do_normalize = codebook_do_normalize
+        self.codebook_image_mean = codebook_image_mean
+        self.codebook_image_mean = codebook_image_mean if codebook_image_mean is not None else FLAVA_CODEBOOK_MEAN
+        self.codebook_image_std = codebook_image_std if codebook_image_std is not None else FLAVA_CODEBOOK_STD
+        self._valid_processor_keys = [
+            "images",
+            "do_resize",
+            "size",
+            "resample",
+            "do_center_crop",
+            "crop_size",
+            "do_rescale",
+            "rescale_factor",
+            "do_normalize",
+            "image_mean",
+            "image_std",
+            "return_image_mask",
+            "input_size_patches",
+            "total_mask_patches",
+            "mask_group_min_patches",
+            "mask_group_max_patches",
+            "mask_group_min_aspect_ratio",
+            "mask_group_max_aspect_ratio",
+            "return_codebook_pixels",
+            "codebook_do_resize",
+            "codebook_size",
+            "codebook_resample",
+            "codebook_do_center_crop",
+            "codebook_crop_size",
+            "codebook_do_rescale",
+            "codebook_rescale_factor",
+            "codebook_do_map_pixels",
+            "codebook_do_normalize",
+            "codebook_image_mean",
+            "codebook_image_std",
+            "return_tensors",
+            "data_format",
+            "input_data_format",
+        ]
+
+    @classmethod
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
+        created using from_dict and kwargs e.g. `FlavaImageProcessor.from_pretrained(checkpoint, codebook_size=600)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "codebook_size" in kwargs:
+            image_processor_dict["codebook_size"] = kwargs.pop("codebook_size")
+        if "codebook_crop_size" in kwargs:
+            image_processor_dict["codebook_crop_size"] = kwargs.pop("codebook_crop_size")
+        return super().from_dict(image_processor_dict, **kwargs)
+
+    @lru_cache()
+    def masking_generator(
+        self,
+        input_size_patches,
+        total_mask_patches,
+        mask_group_min_patches,
+        mask_group_max_patches,
+        mask_group_min_aspect_ratio,
+        mask_group_max_aspect_ratio,
+    ) -> FlavaMaskingGenerator:
+        return FlavaMaskingGenerator(
+            input_size=input_size_patches,
+            total_mask_patches=total_mask_patches,
+            mask_group_min_patches=mask_group_min_patches,
+            mask_group_max_patches=mask_group_max_patches,
+            mask_group_min_aspect_ratio=mask_group_min_aspect_ratio,
+            mask_group_max_aspect_ratio=mask_group_max_aspect_ratio,
+        )
+
+    # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.BICUBIC
+    def resize(
+        self,
+        image: np.ndarray,
+        size: Dict[str, int],
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Resize an image to `(size["height"], size["width"])`.
+
+        Args:
+            image (`np.ndarray`):
+                Image to resize.
+            size (`Dict[str, int]`):
+                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
+            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
+            data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the output image. If unset, the channel dimension format of the input
+                image is used. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+
+        Returns:
+            `np.ndarray`: The resized image.
+        """
+        size = get_size_dict(size)
+        if "height" not in size or "width" not in size:
+            raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
+        output_size = (size["height"], size["width"])
+        return resize(
+            image,
+            size=output_size,
+            resample=resample,
+            data_format=data_format,
+            input_data_format=input_data_format,
+            **kwargs,
+        )
+
+    def map_pixels(self, image: np.ndarray) -> np.ndarray:
+        return (1 - 2 * LOGIT_LAPLACE_EPS) * image + LOGIT_LAPLACE_EPS
+
+    def _preprocess_image(
+        self,
+        image: ImageInput,
+        do_resize: bool = None,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = None,
+        do_center_crop: bool = None,
+        crop_size: Dict[str, int] = None,
+        do_rescale: bool = None,
+        rescale_factor: float = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_map_pixels: bool = None,
+        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+        input_data_format: Optional[ChannelDimension] = None,
+    ) -> np.ndarray:
+        """Preprocesses a single image."""
+
+        validate_preprocess_arguments(
+            do_rescale=do_rescale,
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+            do_center_crop=do_center_crop,
+            crop_size=crop_size,
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+        )
+
+        # All transformations expect numpy arrays.
+        image = to_numpy_array(image)
+
+        if is_scaled_image(image) and do_rescale:
+            logger.warning_once(
+                "It looks like you are trying to rescale already rescaled images. If the input"
+                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+            )
+
+        if input_data_format is None:
+            # We assume that all images have the same channel dimension format.
+            input_data_format = infer_channel_dimension_format(image)
+
+        if do_resize:
+            image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+
+        if do_center_crop:
+            image = self.center_crop(image=image, size=crop_size, input_data_format=input_data_format)
+
+        if do_rescale:
+            image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+
+        if do_normalize:
+            image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+
+        if do_map_pixels:
+            image = self.map_pixels(image)
+
+        if data_format is not None:
+            image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+        return image
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        do_resize: Optional[bool] = None,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = None,
+        do_center_crop: Optional[bool] = None,
+        crop_size: Optional[Dict[str, int]] = None,
+        do_rescale: Optional[bool] = None,
+        rescale_factor: Optional[float] = None,
+        do_normalize: Optional[bool] = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        # Mask related params
+        return_image_mask: Optional[bool] = None,
+        input_size_patches: Optional[int] = None,
+        total_mask_patches: Optional[int] = None,
+        mask_group_min_patches: Optional[int] = None,
+        mask_group_max_patches: Optional[int] = None,
+        mask_group_min_aspect_ratio: Optional[float] = None,
+        mask_group_max_aspect_ratio: Optional[float] = None,
+        # Codebook related params
+        return_codebook_pixels: Optional[bool] = None,
+        codebook_do_resize: Optional[bool] = None,
+        codebook_size: Optional[Dict[str, int]] = None,
+        codebook_resample: Optional[int] = None,
+        codebook_do_center_crop: Optional[bool] = None,
+        codebook_crop_size: Optional[Dict[str, int]] = None,
+        codebook_do_rescale: Optional[bool] = None,
+        codebook_rescale_factor: Optional[float] = None,
+        codebook_do_map_pixels: Optional[bool] = None,
+        codebook_do_normalize: Optional[bool] = None,
+        codebook_image_mean: Optional[Iterable[float]] = None,
+        codebook_image_std: Optional[Iterable[float]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: ChannelDimension = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> PIL.Image.Image:
+        """
+        Preprocess an image or batch of images.
+
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image.
+            resample (`int`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
+                has an effect if `do_resize` is set to `True`.
+            do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
+                Whether to center crop the image.
+            crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
+                Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image values between [0 - 1].
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation.
+            return_image_mask (`bool`, *optional*, defaults to `self.return_image_mask`):
+                Whether to return the image mask.
+            input_size_patches (`int`, *optional*, defaults to `self.input_size_patches`):
+                Size of the patches to extract from the image.
+            total_mask_patches (`int`, *optional*, defaults to `self.total_mask_patches`):
+                Total number of patches to extract from the image.
+            mask_group_min_patches (`int`, *optional*, defaults to `self.mask_group_min_patches`):
+                Minimum number of patches to extract from the image.
+            mask_group_max_patches (`int`, *optional*, defaults to `self.mask_group_max_patches`):
+                Maximum number of patches to extract from the image.
+            mask_group_min_aspect_ratio (`float`, *optional*, defaults to `self.mask_group_min_aspect_ratio`):
+                Minimum aspect ratio of the patches to extract from the image.
+            mask_group_max_aspect_ratio (`float`, *optional*, defaults to `self.mask_group_max_aspect_ratio`):
+                Maximum aspect ratio of the patches to extract from the image.
+            return_codebook_pixels (`bool`, *optional*, defaults to `self.return_codebook_pixels`):
+                Whether to return the codebook pixels.
+            codebook_do_resize (`bool`, *optional*, defaults to `self.codebook_do_resize`):
+                Whether to resize the codebook pixels.
+            codebook_size (`Dict[str, int]`, *optional*, defaults to `self.codebook_size`):
+                Size of the codebook pixels.
+            codebook_resample (`int`, *optional*, defaults to `self.codebook_resample`):
+                Resampling filter to use if resizing the codebook pixels. This can be one of the enum
+                `PILImageResampling`, Only has an effect if `codebook_do_resize` is set to `True`.
+            codebook_do_center_crop (`bool`, *optional*, defaults to `self.codebook_do_center_crop`):
+                Whether to center crop the codebook pixels.
+            codebook_crop_size (`Dict[str, int]`, *optional*, defaults to `self.codebook_crop_size`):
+                Size of the center crop of the codebook pixels. Only has an effect if `codebook_do_center_crop` is set
+                to `True`.
+            codebook_do_rescale (`bool`, *optional*, defaults to `self.codebook_do_rescale`):
+                Whether to rescale the codebook pixels values between [0 - 1].
+            codebook_rescale_factor (`float`, *optional*, defaults to `self.codebook_rescale_factor`):
+                Rescale factor to rescale the codebook pixels by if `codebook_do_rescale` is set to `True`.
+            codebook_do_map_pixels (`bool`, *optional*, defaults to `self.codebook_do_map_pixels`):
+                Whether to map the codebook pixels values.
+            codebook_do_normalize (`bool`, *optional*, defaults to `self.codebook_do_normalize`):
+                Whether to normalize the codebook pixels.
+            codebook_image_mean (`float` or `List[float]`, *optional*, defaults to `self.codebook_image_mean`):
+                Codebook pixels mean to normalize the codebook pixels by if `codebook_do_normalize` is set to `True`.
+            codebook_image_std (`float` or `List[float]`, *optional*, defaults to `self.codebook_image_std`):
+                Codebook pixels standard deviation to normalize the codebook pixels by if `codebook_do_normalize` is
+                set to `True`.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                    - Unset: Return a list of `np.ndarray`.
+                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                    - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                    - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+        """
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        size = size if size is not None else self.size
+        size = get_size_dict(size)
+        resample = resample if resample is not None else self.resample
+        do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
+        crop_size = crop_size if crop_size is not None else self.crop_size
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+
+        return_image_mask = return_image_mask if return_image_mask is not None else self.return_image_mask
+        input_size_patches = input_size_patches if input_size_patches is not None else self.input_size_patches
+        total_mask_patches = total_mask_patches if total_mask_patches is not None else self.total_mask_patches
+        mask_group_min_patches = (
+            mask_group_min_patches if mask_group_min_patches is not None else self.mask_group_min_patches
+        )
+        mask_group_max_patches = (
+            mask_group_max_patches if mask_group_max_patches is not None else self.mask_group_max_patches
+        )
+        mask_group_min_aspect_ratio = (
+            mask_group_min_aspect_ratio
+            if mask_group_min_aspect_ratio is not None
+            else self.mask_group_min_aspect_ratio
+        )
+        mask_group_max_aspect_ratio = (
+            mask_group_max_aspect_ratio
+            if mask_group_max_aspect_ratio is not None
+            else self.mask_group_max_aspect_ratio
+        )
+
+        return_codebook_pixels = (
+            return_codebook_pixels if return_codebook_pixels is not None else self.return_codebook_pixels
+        )
+        codebook_do_resize = codebook_do_resize if codebook_do_resize is not None else self.codebook_do_resize
+        codebook_size = codebook_size if codebook_size is not None else self.codebook_size
+        codebook_size = get_size_dict(codebook_size, param_name="codebook_size")
+        codebook_resample = codebook_resample if codebook_resample is not None else self.codebook_resample
+        codebook_do_rescale = codebook_do_rescale if codebook_do_rescale is not None else self.codebook_do_rescale
+        codebook_rescale_factor = (
+            codebook_rescale_factor if codebook_rescale_factor is not None else self.codebook_rescale_factor
+        )
+        codebook_do_center_crop = (
+            codebook_do_center_crop if codebook_do_center_crop is not None else self.codebook_do_center_crop
+        )
+        codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else self.codebook_crop_size
+        codebook_crop_size = get_size_dict(codebook_crop_size, param_name="codebook_crop_size")
+        codebook_do_map_pixels = (
+            codebook_do_map_pixels if codebook_do_map_pixels is not None else self.codebook_do_map_pixels
+        )
+        codebook_do_normalize = (
+            codebook_do_normalize if codebook_do_normalize is not None else self.codebook_do_normalize
+        )
+        codebook_image_mean = codebook_image_mean if codebook_image_mean is not None else self.codebook_image_mean
+        codebook_image_std = codebook_image_std if codebook_image_std is not None else self.codebook_image_std
+
+        images = make_list_of_images(images)
+
+        validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
+
+        if not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        processed_images = [
+            self._preprocess_image(
+                image=img,
+                do_resize=do_resize,
+                size=size,
+                resample=resample,
+                do_center_crop=do_center_crop,
+                crop_size=crop_size,
+                do_rescale=do_rescale,
+                rescale_factor=rescale_factor,
+                do_normalize=do_normalize,
+                image_mean=image_mean,
+                image_std=image_std,
+                do_map_pixels=False,
+                data_format=data_format,
+                input_data_format=input_data_format,
+            )
+            for img in images
+        ]
+        data = {"pixel_values": processed_images}
+
+        if return_codebook_pixels:
+            codebook_images = [
+                self._preprocess_image(
+                    image=img,
+                    do_resize=codebook_do_resize,
+                    size=codebook_size,
+                    resample=codebook_resample,
+                    do_center_crop=codebook_do_center_crop,
+                    crop_size=codebook_crop_size,
+                    do_rescale=codebook_do_rescale,
+                    rescale_factor=codebook_rescale_factor,
+                    do_normalize=codebook_do_normalize,
+                    image_mean=codebook_image_mean,
+                    image_std=codebook_image_std,
+                    do_map_pixels=codebook_do_map_pixels,
+                    data_format=data_format,
+                    input_data_format=input_data_format,
+                )
+                for img in images
+            ]
+            data["codebook_pixel_values"] = codebook_images
+
+        if return_image_mask:
+            mask_generator = self.masking_generator(
+                input_size_patches=input_size_patches,
+                total_mask_patches=total_mask_patches,
+                mask_group_min_patches=mask_group_min_patches,
+                mask_group_max_patches=mask_group_max_patches,
+                mask_group_min_aspect_ratio=mask_group_min_aspect_ratio,
+                mask_group_max_aspect_ratio=mask_group_max_aspect_ratio,
+            )
+            masks = [mask_generator() for _ in images]
+            data["bool_masked_pos"] = masks
+
+        return BatchFeature(data=data, tensor_type=return_tensors)

venv/lib/python3.10/site-packages/transformers/models/flava/modeling_flava.py

@@ -0,0 +1,2098 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch FLAVA model."""
+
+import collections
+import math
+from collections import OrderedDict
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from ...activations import ACT2FN
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import (
+    ModelOutput,
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_flava import (
+    FlavaConfig,
+    FlavaImageCodebookConfig,
+    FlavaImageConfig,
+    FlavaMultimodalConfig,
+    FlavaTextConfig,
+)
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "facebook/flava-full"
+
+# Codebook docstring
+_CHECKPOINT_FOR_CODEBOOK_DOC = "facebook/flava-image-codebook"
+_CONFIG_CLASS_FOR_IMAGE_MODEL_DOC = "FlavaImageConfig"
+_CONFIG_CLASS_FOR_TEXT_MODEL_DOC = "FlavaTextConfig"
+_CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC = "FlavaMultimodalConfig"
+_EXPECTED_IMAGE_OUTPUT_SHAPE = [1, 197, 768]
+
+from ..deprecated._archive_maps import FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST  # noqa: F401, E402
+
+
+FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST = ["facebook/flava-image-codebook"]
+LOGIT_SCALE_CLAMP_MIN = 0
+LOGIT_SCALE_CLAMP_MAX = 4.6052
+
+FlavaPossibleConfigs = Union[FlavaTextConfig, FlavaImageConfig, FlavaMultimodalConfig]
+
+
+@dataclass
+class FlavaModelOutput(ModelOutput):
+    """
+    Output from FlavaModel containing embeddings and outputs from individual encoders.
+
+    Note that `image_embeddings` and `text_embeddigns` returned are similar to pooled output returned from a
+    transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and
+    `text_projection` layers on `image_embeddings` and `text_embeddings` respectively.
+
+    Args:
+        image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present):
+            The image embeddings which are basically the pooled output of [`FlavaImageModel`].
+        image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present):
+            The output of the [`FlavaImageModel`].
+        text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present):
+            The text embeddings which are basically the pooled output of [`FlavaTextModel`].
+        text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present):
+            The output of the [`FlavaTextModel`].
+        multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`):
+            The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`].
+        multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`):
+            The output of the [`FlavaMultimodalModel`].
+    """
+
+    image_embeddings: Optional[torch.FloatTensor] = None
+    image_output: Optional[BaseModelOutputWithPooling] = None
+    text_embeddings: Optional[torch.FloatTensor] = None
+    text_output: Optional[BaseModelOutputWithPooling] = None
+    multimodal_embeddings: Optional[torch.FloatTensor] = None
+    multimodal_output: Optional[BaseModelOutputWithPooling] = None
+
+    def to_tuple(self) -> Tuple[Any]:
+        return tuple(
+            self[k] if k not in ["text_output", "image_output", "multimodal_output"] else getattr(self, k).to_tuple()
+            for k in self.keys()
+        )
+
+
+@dataclass
+class FlavaLosses(ModelOutput):
+    """Class representing pretraining losses from FLAVA model
+
+    Args:
+        mim (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels` and `pixel_values` are present, `input_ids_masked` is absent and `mim_weight` > 0.:
+            Masked Image Modeling loss as used in BeIT calculated only for unimodal image data.
+        mlm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels` and `input_ids_masked` are present, `pixel_values` is absent and `mlm_weight` > 0.:
+            Masked Language Modeling loss as used in BERT calculated only for unimodal text data.
+        itm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `itm_labels`, `input_ids_masked`, `pixel_values` are present and `itm_weight` > 0.:
+            Image Text Matching (ITM) loss calculated for paired image-text data. Note that ITM loss is calculated on
+            masked pairs in FLAVA.
+        global_contrastive (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `input_ids` and `pixel_values` are present and `global_contrastive_weight` > 0.:
+            Contrastive loss for image-text similarity similar to CLIP but calculated globally for paired image-text
+            data. This is calculated on unmasked images and texts.
+        mmm_image (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_image_weight` > 0.:
+            Masked Multimodal Modeling loss's image component calculated on paired image-text data.
+        mmm_text (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_text_weight` > 0.:
+            Masked Multimodal Modeling loss's text component calculated on paired image-text data.
+    """
+
+    mim: Optional[torch.FloatTensor] = None
+    mlm: Optional[torch.FloatTensor] = None
+    itm: Optional[torch.FloatTensor] = None
+    global_contrastive: Optional[torch.FloatTensor] = None
+    mmm_image: Optional[torch.FloatTensor] = None
+    mmm_text: Optional[torch.FloatTensor] = None
+
+    def all_none(self) -> bool:
+        all_none = True
+        for v in self.values():
+            if v is not None:
+                all_none = False
+                break
+        return all_none
+
+
+@dataclass
+class FlavaForPreTrainingOutput(ModelOutput):
+    """
+    Output from FlavaForPreTraining containing embeddings, and outputs from individual encoders.
+
+    Note that `image_embeddings` and `text_embeddings` returned are similar to pooled output returned from a
+    transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and
+    `text_projection` layers on `image_embeddings` and `text_embeddings` respectively.
+
+    Args:
+        loss (`torch.FloatTensor`, *optional*, returned when `return_loss` is True):
+            Total loss calculated for this model.
+        loss_info (`FlavaLosses`):
+            Detailed info for FLAVA Pretraining losses. Check `FlavaLosses` class description for the information on
+            the keys.
+        image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present):
+            The image embeddings which are basically the pooled output of [`FlavaImageModel`].
+        image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present):
+            The output of the [`FlavaImageModel`].
+        text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present):
+            The text embeddings which are basically the pooled output of [`FlavaTextModel`].
+        text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present):
+            The output of the [`FlavaTextModel`].
+        multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`):
+            The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`].
+        multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`):
+            The output of the [`FlavaMultimodalModel`].
+
+        image_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present):
+            The image embeddings which are basically the pooled output of [`FlavaImageModel`]. Uses `bool_masked_pos`
+            to create masked images.
+        image_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present):
+            The output of the [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images.
+        text_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids_masked` are present):
+            The text embeddings which are basically the pooled output of [`FlavaTextModel`].
+        text_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids_masked` are present):
+            The output of the [`FlavaTextModel`].
+        multimodal_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present):
+            The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`].
+        multimodal_masked_output (`BaseModelOutputWithPooling`, returned when `input_ids_masked` and `pixel_values` are present):
+            The output of the [`FlavaMultimodalModel`].
+
+        mim_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape `(total_masked_patches, image_vocab_size)` , *optional*, returned when `pixel_values` are present and `input_ids_masked` are not):
+                The logits for MIM unimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is
+                returned when `bool_masked_pos` has some of the patches masked.
+        mlm_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(total_masked_seq_length, text_vocab_size)`, *optional*, returned when `input_ids_masked` are present and `pixel_values` are not):
+                The logits for MLM unimodal loss. The flattened output is returned when `input_ids_masked` has some of
+                the tokens masked.
+        itm_logits (`torch.FloatTensor` of shape `(batch_size, 2)`, *optional*, returned when `input_ids_masked` and `pixel_values` are present):
+                The logits for ITM loss. Note that ITM loss is calculated on masked pairs in FLAVA.
+        mmm_image_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape`(total_masked_patches, image_vocab_size)`, *optional*, returned when `pixel_values` and `input_ids_masked` are present):
+                The logits for MMM image multimodal loss. Uses `book_masked_pos` to get masked patches. The flattened
+                output is returned when `bool_masked_pos` has some of the patches masked.
+        mmm_text_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(`(total_masked_seq_length, text_vocab_size)`), *optional*, returned when `pixel_values` and `input_ids_masked` are present):
+                The logits for MMM text multimodal loss. The flattened output is returned when `input_ids_masked` has
+                some of the tokens masked.
+        contrastive_logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
+            The scaled dot product scores between `image_embeddings` and `text_embeddings` but passed through FLAVA's
+            `image_projection` and `text_projection` layers respectively. This represents the image-text similarity
+            scores. This is calculated on unmasked images and texts.
+        contrastive_logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
+            The scaled dot product scores between `text_embeddings` and `image_embeddings` but passed through FLAVA's
+            `text_projection` and `image_projection` layers respectively. This is calculated on unmasked images and
+            texts.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    loss_info: FlavaLosses = None
+    image_embeddings: Optional[torch.FloatTensor] = None
+    image_output: Optional[BaseModelOutputWithPooling] = None
+    text_embeddings: Optional[torch.FloatTensor] = None
+    text_output: Optional[BaseModelOutputWithPooling] = None
+    multimodal_embeddings: Optional[torch.FloatTensor] = None
+    multimodal_output: Optional[BaseModelOutputWithPooling] = None
+    image_masked_embeddings: Optional[torch.FloatTensor] = None
+    image_masked_output: Optional[BaseModelOutputWithPooling] = None
+    text_masked_embeddings: Optional[torch.FloatTensor] = None
+    text_masked_output: Optional[BaseModelOutputWithPooling] = None
+    multimodal_masked_embeddings: Optional[torch.FloatTensor] = None
+    multimodal_masked_output: Optional[BaseModelOutputWithPooling] = None
+    mim_logits: Optional[torch.FloatTensor] = None
+    mlm_logits: Optional[torch.FloatTensor] = None
+    itm_logits: Optional[torch.FloatTensor] = None
+    contrastive_logits_per_image: Optional[torch.FloatTensor] = None
+    contrastive_logits_per_text: Optional[torch.FloatTensor] = None
+    mmm_image_logits: Optional[torch.FloatTensor] = None
+    mmm_text_logits: Optional[torch.FloatTensor] = None
+
+    def to_tuple(self) -> Tuple[Any]:
+        transformer_outputs = [
+            "text_output",
+            "image_output",
+            "multimodal_output",
+            "text_masked_output",
+            "image_masked_output",
+            "multimodal_masked_output",
+        ]
+        return tuple(self[k] if k not in transformer_outputs else getattr(self, k).to_tuple() for k in self.keys())
+
+
+# Based on timm implementation, which can be found here:
+# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py
+class FlavaImageEmbeddings(nn.Module):
+    """
+    Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
+    """
+
+    def __init__(self, config: FlavaImageConfig, use_mask_token: bool = False) -> None:
+        super().__init__()
+
+        use_mask_token = use_mask_token or config.mask_token
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
+        self.patch_embeddings = PatchEmbeddings(
+            image_size=config.image_size,
+            patch_size=config.patch_size,
+            num_channels=config.num_channels,
+            embed_dim=config.hidden_size,
+        )
+        num_patches = self.patch_embeddings.num_patches
+        self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.config = config
+
+    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
+        """
+        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
+        resolution images.
+
+        Source:
+        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/image_transformer.py#L174
+        """
+
+        npatch = embeddings.shape[1] - 1
+        num_pos = self.position_embeddings.shape[1] - 1
+        if npatch == num_pos and height == width:
+            return self.position_embeddings
+        class_pos_embed = self.position_embeddings[:, 0]
+        patch_pos_embed = self.position_embeddings[:, 1:]
+        dim = embeddings.shape[-1]
+        num_h_patches = height // self.config.patch_size
+        num_w_patches = width // self.config.patch_size
+        # we add a small number to avoid floating point error in the interpolation
+        # see discussion at https://github.com/facebookresearch/dino/issues/8
+        num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, int(math.sqrt(num_pos)), int(math.sqrt(num_pos)), dim).permute(0, 3, 1, 2),
+            scale_factor=(num_h_patches / math.sqrt(num_pos), num_w_patches / math.sqrt(num_pos)),
+            mode="bicubic",
+            align_corners=False,
+        )
+        if int(num_h_patches) != patch_pos_embed.shape[-2] or int(num_w_patches) != patch_pos_embed.shape[-1]:
+            raise ValueError(
+                f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the "
+                f"shape of position embedding ({patch_pos_embed.shape[-2], patch_pos_embed.shape[-1]})"
+            )
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        bool_masked_pos: Optional[torch.BoolTensor] = None,
+        interpolate_pos_encoding: bool = False,
+    ) -> torch.Tensor:
+        batch_size, num_channels, height, width = pixel_values.shape
+        embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
+
+        batch_size, seq_len, _ = embeddings.size()
+        if bool_masked_pos is not None:
+            mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
+            # B X H X W = B X HW
+            if bool_masked_pos.dim() == 3:
+                bool_masked_pos = bool_masked_pos.view(bool_masked_pos.size(0), -1)
+            # replace the masked visual tokens by mask_tokens
+            mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
+            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
+
+        # add the [CLS] token to the embedded patch tokens
+        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+        embeddings = torch.cat((cls_tokens, embeddings), dim=1)
+
+        # add positional encoding to each token
+        if interpolate_pos_encoding:
+            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+        else:
+            embeddings = embeddings + self.position_embeddings
+
+        embeddings = self.dropout(embeddings)
+
+        return embeddings
+
+
+# Based on timm implementation, which can be found here:
+# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py
+class PatchEmbeddings(nn.Module):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(
+        self,
+        image_size: int = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        num_channels: int = 3,
+        embed_dim: int = 768,
+    ):
+        super().__init__()
+        if not isinstance(image_size, collections.abc.Iterable):
+            image_size = (image_size, image_size)
+        if not isinstance(patch_size, collections.abc.Iterable):
+            patch_size = (patch_size, patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
+        batch_size, num_channels, height, width = pixel_values.shape
+        if not interpolate_pos_encoding:
+            if height != self.image_size[0] or width != self.image_size[1]:
+                raise ValueError(
+                    f"Input image size ({height}*{width}) doesn't match model"
+                    f" ({self.image_size[0]}*{self.image_size[1]})."
+                )
+        x = self.projection(pixel_values).flatten(2).transpose(1, 2)
+        return x
+
+
+class FlavaTextEmbeddings(nn.Module):
+    """Construct the embeddings from word, position and token_type embeddings."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+        self.register_buffer(
+            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+        )
+        self.register_buffer(
+            "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
+        )
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+    ):
+        input_shape = input_ids.size()
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, :seq_length]
+
+        # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
+        # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
+        # issue #5664
+        if token_type_ids is None:
+            if hasattr(self, "token_type_ids"):
+                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
+                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
+                token_type_ids = buffered_token_type_ids_expanded
+            else:
+                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+        inputs_embeds = self.word_embeddings(input_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + token_type_embeddings
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings += position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class FlavaSelfAttention(nn.Module):
+    def __init__(self, config: FlavaPossibleConfigs) -> None:
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
+                f"heads {config.num_attention_heads}."
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        mixed_query_layer = self.query(hidden_states)
+
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+        return outputs
+
+
+class FlavaSelfOutput(nn.Module):
+    """
+    The residual connection is defined in FlavaLayer (same as ViTLayer) instead of here (as is the case with other
+    models), due to the layernorm applied before each block.
+    """
+
+    def __init__(self, config: FlavaPossibleConfigs) -> None:
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+
+        return hidden_states
+
+
+class FlavaAttention(nn.Module):
+    def __init__(self, config: FlavaPossibleConfigs) -> None:
+        super().__init__()
+        self.attention = FlavaSelfAttention(config)
+        self.output = FlavaSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads: Set[int]) -> None:
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.attention.query = prune_linear_layer(self.attention.query, index)
+        self.attention.key = prune_linear_layer(self.attention.key, index)
+        self.attention.value = prune_linear_layer(self.attention.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
+        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        self_outputs = self.attention(
+            hidden_states, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions
+        )
+
+        attention_output = self.output(self_outputs[0], hidden_states)
+
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+class FlavaIntermediate(nn.Module):
+    def __init__(self, config: FlavaPossibleConfigs) -> None:
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    # Copied from transformers.models.vit.modeling_vit.ViTIntermediate.forward
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+
+        return hidden_states
+
+
+class FlavaOutput(nn.Module):
+    def __init__(self, config: FlavaPossibleConfigs) -> None:
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    # Copied from transformers.models.vit.modeling_vit.ViTOutput.forward
+    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+
+        hidden_states = hidden_states + input_tensor
+
+        return hidden_states
+
+
+class FlavaLayer(nn.Module):
+    """This corresponds to the Block class in the timm implementation."""
+
+    def __init__(self, config: FlavaPossibleConfigs) -> None:
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = FlavaAttention(config)
+        self.intermediate = FlavaIntermediate(config)
+        self.output = FlavaOutput(config)
+
+        # TODO: Check fp32 layer norm possiblity
+        self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        self_attention_outputs = self.attention(
+            self.layernorm_before(hidden_states),  # in ViT, layernorm is applied before self-attention
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+        )
+        attention_output = self_attention_outputs[0]
+        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
+
+        # first residual connection
+        hidden_states = attention_output + hidden_states
+
+        # in ViT, layernorm is also applied after self-attention
+        layer_output = self.layernorm_after(hidden_states)
+        layer_output = self.intermediate(layer_output)
+
+        # second residual connection is done here
+        layer_output = self.output(layer_output, hidden_states)
+
+        outputs = (layer_output,) + outputs
+
+        return outputs
+
+
+class FlavaEncoder(nn.Module):
+    def __init__(self, config: FlavaConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([FlavaLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ) -> Union[tuple, BaseModelOutput]:
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    layer_module.__call__,
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    output_attentions,
+                )
+            else:
+                layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions)
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions
+        )
+
+
+class FlavaPooler(nn.Module):
+    def __init__(self, config: FlavaPossibleConfigs):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states: torch.Tensor):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+FLAVA_START_DOCSTRING = r"""
+    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
+    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
+    behavior.
+
+    Parameters:
+        config ([`{config}`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+FLAVA_INPUTS_DOCSTRING_COMMON = r"""
+        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+            [What are attention masks?](../glossary#attention-mask)
+
+        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+FLAVA_IMAGE_INPUTS_DOCSTRING_BASE = r"""
+    Args:
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
+            [`FlavaImageProcessor.__call__`] for details.
+
+        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, image_num_patches)`):
+            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
+
+        interpolate_pos_encoding (`bool`, *optional*):
+            Whether to interpolate the pre-trained position encodings.
+"""
+
+FLAVA_IMAGE_INPUTS_DOCSTRING = FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON
+
+FLAVA_TEXT_INPUTS_DOCSTRING_BASE = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `({0})`):
+            Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See
+            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
+            IDs?](../glossary#input-ids)
+
+        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+            1]`:
+            - 0 corresponds to a *sentence A* token,
+            - 1 corresponds to a *sentence B* token.
+            [What are token type IDs?](../glossary#token-type-ids)
+"""
+
+FLAVA_TEXT_INPUTS_DOCSTRING = FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON
+
+FLAVA_MULTIMODAL_INPUTS_DOCSTRING = (
+    r"""
+    Args:
+        hidden_states (`torch.FloatTensor` of shape `(batch_size, image_num_patches + text_seq_len, hidden_size)`):
+            The concatenated hidden states of unimodal encoders.
+"""
+    + FLAVA_INPUTS_DOCSTRING_COMMON
+)
+
+FLAVA_MODEL_INPUTS_DOCSTRING_BASE = r"""
+    Args:
+        skip_multimodal_encoder (*bool*, *optional*):
+            Skip any calculations for multimodal encoder. Useful if multimodal encoding is not going to be used.
+"""
+
+FLAVA_MODEL_INPUTS_DOCSTRING = (
+    FLAVA_IMAGE_INPUTS_DOCSTRING_BASE
+    + FLAVA_TEXT_INPUTS_DOCSTRING_BASE
+    + FLAVA_INPUTS_DOCSTRING_COMMON
+    + FLAVA_MODEL_INPUTS_DOCSTRING_BASE
+)
+
+
+FLAVA_PRETRAINING_INPUTS_DOCSTRING = (
+    r"""
+    Args:
+        input_ids_masked (`torch.LongTensor` of shape `({0})`):
+            Indices of input sequence tokens in the vocabulary. These ones are the masked version of the original task
+            to be used with MLM. Indices can be obtained using [`AutoTokenizer`] along with
+            [`DataCollatorForMaskedLanguageModeling`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids)
+
+"""
+    + FLAVA_TEXT_INPUTS_DOCSTRING_BASE
+    + FLAVA_IMAGE_INPUTS_DOCSTRING_BASE
+    + r"""
+        image_attention_mask (`torch.FloatTensor` of shape `({1})`, *optional*):
+            Mask to avoid performing attention on padding token indices specifically for images. Mask values selected
+            in `[0, 1]`:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+            [What are attention masks?](../glossary#attention-mask)
+
+        skip_unmasked_multimodal_encoder (*bool*, *optional*):
+            Skip any calculations for multimodal encoder for unmasked inputs. FLAVA pretraining doesn't need unmasked
+            multimodal embeddings or outputs as of now.
+
+        mlm_labels (`torch.LongTensor` of shape `(batch_size, text_seq_len)`, *optional*):
+            Labels for computing the left-to-right language and multimodal masked modeling loss (next word prediction).
+            Indices should be in `[-100, 0, ..., text_config.vocab_size - 1]` (see `input_ids` docstring). Tokens with
+            indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0,
+            ..., text_config.vocab_size - 1]`.
+
+        mim_labels (`torch.LongTensor` of shape `(batch_size, image_num_patches)`, *optional*):
+            Labels for computing the image and multimodal masked modeling loss. Indices should be in `[-100, 0, ...,
+            image_config.vocab_size - 1]`. Tokens with indices set to `-100` are ignored (masked), the loss is only
+            computed for the tokens with labels in `[0, ..., image_config.vocab_size - 1]`. If not passed, they are
+            generated automatically using the image codebook assigned to the model. By default, it uses
+            [`FlavaImageCodebook`]. See [`FlavaImageCodebook`] to understand how to generate mim_labels.
+
+        itm_labels (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*):
+            Labels for computing the image-text matching loss. 0 means the pairs don't match and 1 means they match.
+            The pairs with 0 will be skipped for calculation of MMM and global contrastive losses as well.
+
+        return_loss (`bool`, *optional*, default to None):
+            Whether to return calculated loss or not.
+"""
+    + FLAVA_INPUTS_DOCSTRING_COMMON
+)
+
+FLAVA_PRETRAINING_START_DOCSTRING_EXTRA = r"""
+    Parameters:
+        image_codebook ([`nn.Module`]): If passed, the image codebook will be set to this. Otherwise. it will
+            be initialized using the image_codebook_config defined in the config first as the first parameter.
+"""
+
+
+class FlavaPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = FlavaConfig
+    base_model_prefix = "flava"
+    supports_gradient_checkpointing = True
+
+    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
+        """Initialize the weights"""
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+@add_start_docstrings(
+    "The bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top.",
+    FLAVA_START_DOCSTRING.format(config="FlavaImageConfig"),
+)
+class FlavaImageModel(FlavaPreTrainedModel):
+    config_class = FlavaImageConfig
+    # This override allows us to load FlavaImageModel from FlavaModel/FlavaForPreTraining checkpoints.
+    base_model_prefix = "flava.image_model"
+    main_input_name = "pixel_values"
+
+    def __init__(self, config: FlavaImageConfig, add_pooling_layer: bool = True):
+        super().__init__(config)
+
+        self.config = config
+
+        self.embeddings = FlavaImageEmbeddings(config)
+        self.encoder = FlavaEncoder(config)
+
+        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.pooler = FlavaPooler(config) if add_pooling_layer else None
+
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.embeddings.patch_embeddings
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.embeddings.patch_embeddings = value
+
+    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    @add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPooling,
+        config_class=_CONFIG_CLASS_FOR_IMAGE_MODEL_DOC,
+        modality="vision",
+        expected_output=_EXPECTED_IMAGE_OUTPUT_SHAPE,
+    )
+    def forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        bool_masked_pos: Optional[torch.BoolTensor] = None,
+        interpolate_pos_encoding: Optional[bool] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple, BaseModelOutputWithPooling]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if pixel_values is None:
+            raise ValueError("You have to specify pixel_values")
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
+        )
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        sequence_output = self.layernorm(sequence_output)
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    "The bare FLAVA Text Model transformer outputting raw hidden-states without any specific head on top.",
+    FLAVA_START_DOCSTRING.format(config="FlavaTextConfig"),
+)
+class FlavaTextModel(FlavaPreTrainedModel):
+    config_class = FlavaTextConfig
+    # This override allows us to load FlavaTextModel from FlavaModel/FlavaForPreTraining checkpoints.
+    base_model_prefix = "flava.text_model"
+
+    def __init__(self, config: FlavaTextConfig, add_pooling_layer: bool = True):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = FlavaTextEmbeddings(config)
+        self.encoder = FlavaEncoder(config)
+
+        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.pooler = FlavaPooler(config) if add_pooling_layer else None
+
+        self.post_init()
+
+    def get_input_embeddings(self) -> PatchEmbeddings:
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.embeddings.word_embeddings = value
+
+    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    @add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPooling,
+        config_class=_CONFIG_CLASS_FOR_TEXT_MODEL_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple, BaseModelOutputWithPooling]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is None:
+            raise ValueError("You have to specify input_ids")
+
+        input_shape = input_ids.size()
+
+        if attention_mask is None:
+            attention_mask = torch.ones(input_shape, device=input_ids.device)
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+            attention_mask, input_shape, input_ids.device
+        )
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+        )
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        sequence_output = self.layernorm(sequence_output)
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    "The bare FLAVA Multimodal Model transformer outputting raw hidden-states without any specific head on top.",
+    FLAVA_START_DOCSTRING.format(config="FlavaMultimodalConfig"),
+)
+class FlavaMultimodalModel(FlavaPreTrainedModel):
+    config_class = FlavaMultimodalConfig
+    # This override allows us to load FlavaMultimodalModel from FlavaModel/FlavaForPreTraining checkpoints.
+    base_model_prefix = "flava.multimodal_model"
+    main_input_name = "hidden_states"
+
+    def __init__(self, config: FlavaMultimodalConfig, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+        self.use_cls_token = self.config.use_cls_token
+        if self.use_cls_token:
+            self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+
+        self.encoder = FlavaEncoder(config)
+
+        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.pooler = FlavaPooler(config) if add_pooling_layer else None
+
+        self.post_init()
+
+    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    @add_start_docstrings_to_model_forward(
+        FLAVA_MULTIMODAL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len")
+    )
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPooling,
+        config_class=_CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC,
+    )
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple, BaseModelOutputWithPooling]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        batch_size, seq_length, _ = hidden_states.size()
+
+        if self.use_cls_token:
+            cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+            hidden_states = torch.cat((cls_tokens, hidden_states), dim=1)
+            seq_length += 1
+
+        if attention_mask is None:
+            attention_mask = torch.ones((batch_size, seq_length), device=hidden_states.device)
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+            attention_mask, (batch_size, seq_length), hidden_states.device
+        )
+
+        encoder_outputs = self.encoder(
+            hidden_states,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        sequence_output = self.layernorm(sequence_output)
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    "The bare FLAVA Model transformer outputting raw hidden-states without any specific head on top.",
+    FLAVA_START_DOCSTRING.format(config="FlavaConfig"),
+)
+class FlavaModel(FlavaPreTrainedModel):
+    config_class = FlavaConfig
+
+    def __init__(self, config: FlavaConfig):
+        super().__init__(config)
+
+        if not isinstance(config.text_config, FlavaTextConfig):
+            raise ValueError(
+                "config.text_config is expected to be of type FlavaTextConfig but is of type"
+                f" {type(config.text_config)}."
+            )
+
+        if not isinstance(config.image_config, FlavaImageConfig):
+            raise ValueError(
+                "config.image_config is expected to be of type FlavaImageConfig but is of type"
+                f" {type(config.image_config)}."
+            )
+
+        if not isinstance(config.multimodal_config, FlavaMultimodalConfig):
+            raise ValueError(
+                "config.multimodal_config is expected to be of type FlavaMultimodalConfig but "
+                + f"is of type {type(config.multimodal_config)}."
+            )
+
+        text_config = config.text_config
+        image_config = config.image_config
+        multimodal_config = config.multimodal_config
+
+        self.projection_dim = config.projection_dim
+        self.text_hidden_size = text_config.hidden_size
+        self.image_hidden_size = image_config.hidden_size
+        self.mm_hidden_size = multimodal_config.hidden_size
+
+        self.text_model = FlavaTextModel(text_config)
+        self.image_model = FlavaImageModel(image_config)
+        self.multimodal_model = FlavaMultimodalModel(multimodal_config)
+
+        self.image_projection = nn.Linear(self.image_hidden_size, self.projection_dim)
+        self.text_projection = nn.Linear(self.text_hidden_size, self.projection_dim)
+        self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
+
+        self.image_to_mm_projection = nn.Linear(self.image_hidden_size, self.mm_hidden_size)
+        self.text_to_mm_projection = nn.Linear(self.text_hidden_size, self.mm_hidden_size)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length"))
+    def get_text_features(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> torch.FloatTensor:
+        r"""
+        Returns:
+            text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
+            applying the projection layer to the pooled output of [`FlavaTextModel`].
+
+        Examples:
+
+        ```python
+        >>> from transformers import AutoProcessor, FlavaModel
+
+        >>> model = FlavaModel.from_pretrained("{0}")
+        >>> processor = AutoProcessor.from_pretrained("{0}")
+
+        >>> inputs = processor(
+        ...     text=["a photo of a cat", "a photo of a dog"], max_length=77, padding="max_length", return_tensors="pt"
+        ... )
+        >>> text_features = model.get_text_features(**inputs)
+        ```""".format(_CHECKPOINT_FOR_DOC)
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = text_outputs[0]  # last_hidden_state
+        text_features = self.text_projection(pooled_output)
+
+        return text_features
+
+    @add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches"))
+    def get_image_features(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        bool_masked_pos: Optional[torch.BoolTensor] = None,
+        interpolate_pos_encoding: Optional[bool] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> torch.FloatTensor:
+        r"""
+        Returns:
+            image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
+            applying the projection layer to the pooled output of [`FlavaImageModel`].
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, FlavaModel
+
+        >>> model = FlavaModel.from_pretrained("{0}")
+        >>> processor = AutoProcessor.from_pretrained("{0}")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = processor(images=image, return_tensors="pt")
+
+        >>> image_features = model.get_image_features(**inputs)
+        ```""".format(_CHECKPOINT_FOR_DOC)
+        image_outputs = self.image_model(
+            pixel_values=pixel_values,
+            bool_masked_pos=bool_masked_pos,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            return_dict=return_dict,
+        )
+
+        pooled_output = image_outputs[0]  # last_hidden_state
+        image_features = self.image_projection(pooled_output)
+
+        return image_features
+
+    @add_start_docstrings_to_model_forward(
+        FLAVA_MODEL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len")
+    )
+    @replace_return_docstrings(output_type=FlavaModelOutput, config_class=FlavaConfig)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        bool_masked_pos: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        image_attention_mask: Optional[torch.Tensor] = None,
+        skip_multimodal_encoder: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: bool = True,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, FlavaOutput]:
+        r"""
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, FlavaModel
+
+        >>> model = FlavaModel.from_pretrained("facebook/flava-full")
+        >>> processor = AutoProcessor.from_pretrained("facebook/flava-full")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = processor(text=["a photo of a cat"], images=image, return_tensors="pt", padding=True)
+
+        >>> outputs = model(**inputs)
+
+        >>> image_embeddings = outputs.image_embeddings
+        >>> text_embeddings = outputs.text_embeddings
+        >>> multimodal_embeddings = outputs.multimodal_embeddings
+
+        >>> outputs.image_embeddings.shape
+        torch.Size([1, 197, 768])
+
+        >>> text_embeddings.shape
+        torch.Size([1, 7, 768])
+
+        >>> multimodal_embeddings.shape
+        torch.Size([1, 205, 768])
+        ```
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+        if not output_hidden_states:
+            raise ValueError("FLAVA model requires hidden states to work. Please set `output_hidden_states=True`")
+        image_embeddings = None
+        image_states = None
+        image_mm_projection = None
+        image_output = None
+        if pixel_values is not None:
+            image_output = self.image_model(
+                pixel_values=pixel_values,
+                bool_masked_pos=bool_masked_pos,
+                attention_mask=image_attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            image_embeddings, image_states = image_output[0], image_output[2]
+            # Note that these states don't use final layernorm in the transformer model
+            image_mm_projection = self.image_to_mm_projection(image_states[-1])
+
+        text_embeddings = None
+        text_states = None
+        text_mm_projection = None
+        text_output = None
+        if input_ids is not None:
+            text_output = self.text_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                token_type_ids=token_type_ids,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            text_embeddings, text_states = text_output[0], text_output[2]
+            # Note that these states don't use final layernorm in the transformer model
+            text_mm_projection = self.text_to_mm_projection(text_states[-1])
+
+        multimodal_embeddings = None
+        multimodal_output = None
+        if image_mm_projection is not None and text_mm_projection is not None and not skip_multimodal_encoder:
+            if attention_mask is not None:
+                batch_size, seq_len, _ = image_mm_projection.shape
+                if self.multimodal_model.use_cls_token:
+                    seq_len += 1
+                attention_mask_image = torch.ones(batch_size, seq_len, device=image_mm_projection.device)
+                attention_multimodal = torch.cat([attention_mask_image, attention_mask], dim=1)
+            else:
+                attention_multimodal = None
+            multimodal_input = torch.cat([image_mm_projection, text_mm_projection], dim=1)
+            multimodal_output = self.multimodal_model(
+                multimodal_input, attention_mask=attention_multimodal, return_dict=return_dict
+            )
+            multimodal_embeddings = multimodal_output[0]
+
+        if not return_dict:
+            return (
+                image_embeddings,
+                image_output,
+                text_embeddings,
+                text_output,
+                multimodal_embeddings,
+                multimodal_output,
+            )
+
+        return FlavaModelOutput(
+            image_embeddings=image_embeddings,
+            image_output=image_output,
+            text_embeddings=text_embeddings,
+            text_output=text_output,
+            multimodal_embeddings=multimodal_embeddings,
+            multimodal_output=multimodal_output,
+        )
+
+
+class FlavaImageCodebookResPath(nn.Module):
+    def __init__(self, in_size: int, out_size: int, **kwargs):
+        super().__init__()
+        hid_size = out_size // 4
+
+        path = OrderedDict()
+        path["relu_1"] = nn.ReLU()
+        path["conv_1"] = nn.Conv2d(in_size, hid_size, kernel_size=3, padding=1)
+        path["relu_2"] = nn.ReLU()
+        path["conv_2"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1)
+        path["relu_3"] = nn.ReLU()
+        path["conv_3"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1)
+        path["relu_4"] = nn.ReLU()
+        path["conv_4"] = nn.Conv2d(hid_size, out_size, kernel_size=1, padding=0)
+
+        self.path = nn.Sequential(path)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.path(x)
+
+
+class FlavaImageCodebookBlock(nn.Module):
+    def __init__(self, in_size: int, out_size: int, num_layers: int, **kwargs):
+        super().__init__()
+
+        self.post_gain = 1 / (num_layers**2)
+
+        if in_size != out_size:
+            self.id_path = nn.Conv2d(in_size, out_size, kernel_size=1, padding=0)
+        else:
+            self.id_path = nn.Identity()
+
+        self.res_path = FlavaImageCodebookResPath(in_size, out_size)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.id_path(x) + self.post_gain * self.res_path(x)
+
+
+class FlavaImageCodebookLayerGroup(nn.Module):
+    def __init__(self, num_blocks: int, num_layers: int, in_size: int, out_size: int, use_pool: bool = True):
+        super().__init__()
+        blocks = OrderedDict()
+        for i in range(num_blocks):
+            if i == 0:
+                blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(in_size, out_size, num_layers)
+            else:
+                blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(out_size, out_size, num_layers)
+
+        if use_pool:
+            blocks["pool"] = nn.MaxPool2d(kernel_size=2)
+
+        self.group = nn.Sequential(blocks)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.group(x)
+
+
+# Inspired by DALLE Encoder in https://github.com/openai/DALL-E/blob/5be4b236bc3ade6943662354117a0e83752cc322/dall_e/encoder.py#L42
+@add_start_docstrings(
+    """
+    The FLAVA's image codebook model inspired from DALL-E's original encoder. Outputs raw hidden states and can be used
+    to generate image tokens for an image based on DALL-E's vocab. Used to generate labels for MIM. Use
+    `get_codebook_indices` to get image tokens for an image.
+    """,
+    FLAVA_START_DOCSTRING.format(config="FlavaImageCodebookConfig"),
+)
+class FlavaImageCodebook(FlavaPreTrainedModel):
+    base_model_prefix = ""
+    config_class = FlavaImageCodebookConfig
+    main_input_name = "pixel_values"
+    supports_gradient_checkpointing = False
+
+    def __init__(
+        self,
+        config: FlavaImageCodebookConfig,
+        **kwargs: Any,
+    ):
+        super().__init__(config)
+
+        self.config = config
+        self.num_groups = config.num_groups
+        self.input_channels = config.input_channels
+        self.num_blocks_per_group = config.num_blocks_per_group
+        self.hidden_size = config.hidden_size
+        self.vocab_size = config.vocab_size
+
+        num_layers = self.num_groups * self.num_blocks_per_group
+
+        output_blocks = OrderedDict()
+        output_blocks["relu"] = nn.ReLU()
+        output_blocks["conv"] = nn.Conv2d(8 * self.hidden_size, self.vocab_size, kernel_size=1, padding=0)
+
+        blocks = OrderedDict()
+        blocks["input"] = nn.Conv2d(self.input_channels, 1 * self.hidden_size, kernel_size=7, padding=3)
+        blocks["group_1"] = FlavaImageCodebookLayerGroup(
+            self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 1 * self.hidden_size
+        )
+        blocks["group_2"] = FlavaImageCodebookLayerGroup(
+            self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 2 * self.hidden_size
+        )
+        blocks["group_3"] = FlavaImageCodebookLayerGroup(
+            self.num_blocks_per_group, num_layers, 2 * self.hidden_size, 4 * self.hidden_size
+        )
+        blocks["group_4"] = FlavaImageCodebookLayerGroup(
+            self.num_blocks_per_group, num_layers, 4 * self.hidden_size, 8 * self.hidden_size, use_pool=False
+        )
+        blocks["output"] = nn.Sequential(output_blocks)
+
+        self.blocks = nn.Sequential(blocks)
+
+        self.post_init()
+
+        if self.config.freeze:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def get_codebook_indices(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+                Pixel values. Codebook pixel values can be obtained using [`AutoImageProcessor`] by passing
+                `return_codebook_pixels=True`. See [`FlavaImageProcessor.__call__`] for details.
+
+        Examples:
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoImageProcessor, FlavaImageCodebook
+
+        >>> model = FlavaImageCodebook.from_pretrained("{0}")
+        >>> image_processor = AutoImageProcessor.from_pretrained("{0}")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = image_processor([image], return_codebook_pixels=True, return_tensors="pt")
+        >>> inputs = dict(pixel_values=inputs.codebook_pixel_values)
+
+        >>> outputs = model.get_codebook_indices(**inputs)
+        ```
+        """.format(_CHECKPOINT_FOR_CODEBOOK_DOC)
+        z_logits = self.blocks(pixel_values)
+        return torch.argmax(z_logits, axis=1)
+
+    def get_codebook_probs(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        z_logits = self.blocks(pixel_values)
+        return nn.Softmax(dim=1)(z_logits)
+
+    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+        """
+        Args:
+            pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+                Pixel values. Codebook pixel values can be obtained using [`AutoImageProcessor`] by passing
+                `return_codebook_pixels=True`. See [`FlavaImageProcessor.__call__`] for details.
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoImageProcessor, FlavaImageCodebook
+
+        >>> model = FlavaImageCodebook.from_pretrained("{0}")
+        >>> image_processor = AutoImageProcessor.from_pretrained("{0}")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = image_processor([image], return_codebook_pixels=True, return_tensors="pt")
+        >>> inputs = dict(pixel_values=inputs.codebook_pixel_values)
+
+        >>> outputs = model(**inputs)
+        >>> print(outputs.shape)
+        (1, 196)
+        ```
+        """.format(_CHECKPOINT_FOR_CODEBOOK_DOC)
+        if len(pixel_values.shape) != 4:
+            raise ValueError(f"input shape {pixel_values.shape} is not 4d")
+        if pixel_values.shape[1] != self.input_channels:
+            raise ValueError(f"input has {pixel_values.shape[1]} channels but model built for {self.input_channels}")
+        return self.blocks(pixel_values)
+
+
+class FlavaPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.transform_act_fn = config.hidden_act
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class FlavaMaskedPredictionHead(nn.Module):
+    def __init__(self, config, weight=None):
+        super().__init__()
+        self.config = config
+        self.transform = FlavaPredictionHeadTransform(config)
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+        if weight is not None:
+            self.decoder.weight = weight
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, x):
+        x = self.transform(x)
+        x = self.decoder(x)
+        return x
+
+
+class FlavaITMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.pooler = FlavaPooler(config)
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, x):
+        x = self.pooler(x)
+        x = self.seq_relationship(x)
+        return x
+
+
+class FlavaGlobalContrastiveHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.global_backprop_contrastive = config.global_backprop_contrastive
+
+    def forward(self, image_embeddings, text_embeddings, logit_scale):
+        temperature = torch.exp(logit_scale)
+        if not torch.distributed.is_available() or not torch.distributed.is_initialized():
+            labels = torch.arange(image_embeddings.size(0), device=image_embeddings.device)
+            image_embeddings_all = [image_embeddings]
+            text_embeddings_all = [text_embeddings]
+        else:
+            local_batch_size = image_embeddings.size(0)
+            world_size = torch.distributed.get_world_size()
+
+            if self.global_backprop_contrastive:
+                # `torch.distributed.nn.functional.all_gather` does backprop on all active workers
+                # whereas `torch.distributed.all_gather` does only backpropagates on the current worker.
+                image_embeddings_all = torch.distributed.nn.functional.all_gather(image_embeddings)
+                text_embeddings_all = torch.distributed.nn.functional.all_gather(text_embeddings)
+            else:
+                image_embeddings_all = [torch.zeros_like(text_embeddings) for _ in range(world_size)]
+                text_embeddings_all = [torch.zeros_like(image_embeddings) for _ in range(world_size)]
+                torch.distributed.all_gather(image_embeddings_all, image_embeddings)
+                torch.distributed.all_gather(text_embeddings_all, text_embeddings)
+
+            labels = local_batch_size * torch.distributed.get_rank() + torch.arange(
+                local_batch_size, device=image_embeddings.device
+            )
+
+        image_embeddings_all = torch.cat(image_embeddings_all)
+        text_embeddings_all = torch.cat(text_embeddings_all)
+
+        logits_per_image = torch.matmul(image_embeddings, text_embeddings_all.transpose(0, 1)) * temperature
+        logits_per_text = torch.matmul(text_embeddings, image_embeddings_all.transpose(0, 1)) * temperature
+
+        return logits_per_image, logits_per_text, labels
+
+
+@add_start_docstrings(
+    """
+    The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs.
+    """,
+    FLAVA_START_DOCSTRING.format(config="FlavaConfig") + FLAVA_PRETRAINING_START_DOCSTRING_EXTRA,
+)
+class FlavaForPreTraining(FlavaPreTrainedModel):
+    # Those are linked to xxx.bias
+    _tied_weights_keys = [
+        "mmm_text_head.decoder.bias",
+        "mmm_image_head.decoder.bias",
+        "mlm_head.decoder.bias",
+        "mim_head.decoder.bias",
+    ]
+
+    def __init__(self, config: FlavaConfig, image_codebook: Optional[nn.Module] = None):
+        super().__init__(config)
+        self.flava = FlavaModel(config)
+
+        self.image_codebook = image_codebook
+        if self.image_codebook is None and config.init_codebook:
+            self.image_codebook = FlavaImageCodebook(config.image_codebook_config)
+
+        # Levarage text and image encoder configs to create the masked
+        # head since it has the right vocab
+        self.mim_head = FlavaMaskedPredictionHead(config.image_config)
+        self.mlm_head = FlavaMaskedPredictionHead(config.text_config)
+        self.itm_head = FlavaITMHead(config)
+        self.mmm_image_head = FlavaMaskedPredictionHead(config.image_config)
+        self.mmm_text_head = FlavaMaskedPredictionHead(config.text_config)
+        self.global_contrastive_head = FlavaGlobalContrastiveHead(config)
+
+        self.image_vocab_size = config.image_config.vocab_size
+        self.text_vocab_size = config.text_config.vocab_size
+        self.mlm_weight = config.mlm_weight
+        self.mim_weight = config.mim_weight
+        self.global_contrastive_weight = config.global_contrastive_weight
+        self.ce_ignore_index = config.ce_ignore_index
+        self.itm_weight = config.itm_weight
+        self.mmm_image_weight = config.mmm_image_weight
+        self.mmm_text_weight = config.mmm_text_weight
+        self.skip_unmasked_multimodal_encoder = config.skip_unmasked_multimodal_encoder
+
+        self.post_init()
+
+    def _resize_to_2d(self, x: torch.Tensor):
+        if x.dim() > 2:
+            x = x.view(x.size(0), -1)
+        return x
+
+    @add_start_docstrings_to_model_forward(
+        FLAVA_PRETRAINING_INPUTS_DOCSTRING.format("batch_size, text_seq_len", "batch_size, image_num_patches")
+    )
+    @replace_return_docstrings(output_type=FlavaForPreTrainingOutput, config_class=FlavaConfig)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        input_ids_masked: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        codebook_pixel_values: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        bool_masked_pos: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        image_attention_mask: Optional[torch.Tensor] = None,
+        skip_unmasked_multimodal_encoder: bool = None,
+        mlm_labels: Optional[torch.Tensor] = None,
+        mim_labels: Optional[torch.Tensor] = None,
+        itm_labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: bool = True,
+        return_dict: Optional[bool] = None,
+        return_loss: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], FlavaForPreTrainingOutput]:
+        """
+        Examples:
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import FlavaForPreTraining, AutoProcessor
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> model = FlavaForPreTraining.from_pretrained("facebook/flava-full")
+        >>> processor = AutoProcessor.from_pretrained("facebook/flava-full")
+
+        >>> text = ["a photo of a cat"]
+
+        >>> inputs = processor(
+        ...     images=[image],
+        ...     text=text,
+        ...     return_masks=True,
+        ...     return_codebook_pixels=True,
+        ...     padding=True,
+        ...     max_length=77,
+        ...     return_tensors="pt",
+        ... )
+
+
+        >>> output = model(**inputs)
+        ```
+
+        Return:
+
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        return_loss = return_loss if return_loss is not None else self.config.return_loss
+
+        skip_unmasked_multimodal_encoder = (
+            skip_unmasked_multimodal_encoder
+            if skip_unmasked_multimodal_encoder is not None
+            else self.skip_unmasked_multimodal_encoder
+        )
+
+        if input_ids_masked is None and input_ids is not None:
+            logger.warning(
+                "`input_ids_masked` isn't passed which means MLM loss won't be calculated correctlySetting it to"
+                " `input_ids` so that model can work. Please pass it if this is unintentional. This is usually OKAY if"
+                " you are doing inference on unmasked text..."
+            )
+            input_ids_masked = input_ids
+
+        flava_output = self.flava(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            image_attention_mask=image_attention_mask,
+            # Don't need unmasked multimodal embedding for anything so skip it
+            # NOTE: ITM uses masked version
+            skip_multimodal_encoder=skip_unmasked_multimodal_encoder,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            # Pass true to have deterministic outputs
+            return_dict=True,
+        )
+
+        flava_masked_output = self.flava(
+            input_ids=input_ids_masked,
+            pixel_values=pixel_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            image_attention_mask=image_attention_mask,
+            bool_masked_pos=bool_masked_pos,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+        )
+
+        pos_mask = None
+
+        image_embeddings = flava_output.image_embeddings
+        text_embeddings = flava_output.text_embeddings
+        image_masked_embeddings = flava_masked_output.image_embeddings
+        text_masked_embeddings = flava_masked_output.text_embeddings
+        multimodal_masked_embeddings = flava_masked_output.multimodal_embeddings
+
+        total_loss = mim_loss = mlm_loss = mmm_text_loss = mmm_image_loss = gc_loss = itm_loss = None
+        mim_logits = mlm_logits = mmm_text_logits = mmm_image_logits = None
+        itm_logits = logits_per_image = logits_per_text = None
+
+        # Calculate mim_labels if necessary from the image_codebook
+        if image_masked_embeddings is not None or multimodal_masked_embeddings is not None:
+            if mim_labels is None and return_loss:
+                if self.image_codebook is None:
+                    raise RuntimeError(
+                        "`return_loss` is set to True but the image codebook is not initialized and no `mim_labels` "
+                        " have been passed. Reinstantiate the model with `init_codebook` set to True or "
+                        "pass in your custom `mim_labels`"
+                    )
+                if codebook_pixel_values is None:
+                    raise ValueError(
+                        "`codebook_pixel_value` are required to generate `mim_labels` if loss is expected. "
+                        "Call `AutoProcessor` with `return_codebook_pixels` set to True"
+                    )
+                mim_labels = self.image_codebook.get_codebook_indices(codebook_pixel_values)
+        # Unimodal MIM Loss
+        # If multimodal embeddings are present, we will calculate MMM loss
+        if self.mim_weight > 0 and image_masked_embeddings is not None and multimodal_masked_embeddings is None:
+            sequence_for_image = image_masked_embeddings
+
+            if mim_labels is not None:
+                mim_labels = self._resize_to_2d(mim_labels)
+                bool_masked_pos = self._resize_to_2d(bool_masked_pos)
+                mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index
+
+                sequence_for_image = sequence_for_image[:, -mim_labels.size(1) :, :]
+                masked_tokens = mim_labels.ne(self.ce_ignore_index)
+                mim_labels_filtered = mim_labels[masked_tokens]
+                sequence_for_image = sequence_for_image[masked_tokens, :]
+                mim_logits = self.mim_head(sequence_for_image)
+                if return_loss:
+                    mim_loss = nn.functional.cross_entropy(
+                        mim_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1)
+                    )
+                    mim_loss *= self.mim_weight
+            else:
+                mim_logits = self.mim_head(sequence_for_image)
+
+        # Unimodal MLM Loss
+        if self.mlm_weight > 0 and text_masked_embeddings is not None and multimodal_masked_embeddings is None:
+            sequence_for_text = text_masked_embeddings
+            if mlm_labels is not None:
+                mlm_labels = self._resize_to_2d(mlm_labels)
+                sequence_for_text = sequence_for_text[:, -mlm_labels.size(1) :, :]
+                masked_tokens = mlm_labels.ne(self.ce_ignore_index)
+                mlm_labels_filtered = mlm_labels[masked_tokens]
+                sequence_for_text = sequence_for_text[masked_tokens, :]
+                mlm_logits = self.mlm_head(sequence_for_text)
+                if return_loss:
+                    mlm_loss = nn.functional.cross_entropy(
+                        mlm_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1)
+                    )
+                    mlm_loss *= self.mlm_weight
+            else:
+                mlm_logits = self.mlm_head(sequence_for_text)
+
+        # ITM Loss
+        if self.itm_weight > 0 and multimodal_masked_embeddings is not None:
+            itm_logits = self.itm_head(multimodal_masked_embeddings)
+
+            if itm_labels is not None:
+                pos_pairs = itm_labels.ne(0)
+                pos_mask = torch.where(pos_pairs.any(), pos_pairs, pos_pairs.new([True]))
+                if return_loss:
+                    itm_loss = nn.functional.cross_entropy(itm_logits, itm_labels)
+                    itm_loss *= self.itm_weight
+
+                if multimodal_masked_embeddings is not None:
+                    multimodal_masked_embeddings = multimodal_masked_embeddings[pos_mask]
+
+                if mlm_labels is not None:
+                    mlm_labels = mlm_labels[pos_mask]
+
+                if mim_labels is not None:
+                    mim_labels = mim_labels[pos_mask]
+                    bool_masked_pos = bool_masked_pos[pos_mask]
+
+        # MMM Image Loss
+        if multimodal_masked_embeddings is not None and self.mmm_image_weight > 0:
+            sequence_for_image = multimodal_masked_embeddings
+            end_index = image_masked_embeddings.size(1) - 1
+            sequence_for_image = sequence_for_image[:, 2 : 2 + end_index, :]
+
+            if mim_labels is not None:
+                mim_labels = self._resize_to_2d(mim_labels)
+                bool_masked_pos = self._resize_to_2d(bool_masked_pos)
+                mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index
+
+                masked_tokens = mim_labels.ne(self.ce_ignore_index)
+                mim_labels_filtered = mim_labels[masked_tokens]
+                sequence_for_image = sequence_for_image[masked_tokens, :]
+                mmm_image_logits = self.mmm_image_head(sequence_for_image)
+                if return_loss:
+                    mmm_image_loss = nn.functional.cross_entropy(
+                        mmm_image_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1)
+                    )
+                    mmm_image_loss *= self.mmm_image_weight
+            else:
+                mmm_image_logits = self.mmm_image_head(sequence_for_image)
+
+        # MMM Text Loss
+        if multimodal_masked_embeddings is not None and self.mmm_text_weight > 0:
+            sequence_for_text = multimodal_masked_embeddings
+            sequence_for_text = sequence_for_text[:, -text_masked_embeddings.size(1) :, :]
+
+            if mlm_labels is not None:
+                mlm_labels = self._resize_to_2d(mlm_labels)
+                masked_tokens = mlm_labels.ne(self.ce_ignore_index)
+                mlm_labels_filtered = mlm_labels[masked_tokens]
+                sequence_for_text = sequence_for_text[masked_tokens, :]
+                mmm_text_logits = self.mmm_text_head(sequence_for_text)
+                if return_loss:
+                    mmm_text_loss = nn.functional.cross_entropy(
+                        mmm_text_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1)
+                    )
+                    mmm_text_loss *= self.mmm_text_weight
+            else:
+                mmm_text_logits = self.mmm_text_head(sequence_for_text)
+
+        # Global Contrastive Loss
+        if image_embeddings is not None and text_embeddings is not None and self.global_contrastive_weight > 0:
+            text_embedding = self.flava.text_projection(text_embeddings[:, 0, :])
+            text_embedding = nn.functional.normalize(text_embedding, dim=-1)
+
+            image_embedding = self.flava.image_projection(image_embeddings[:, 0, :])
+            image_embedding = nn.functional.normalize(image_embedding, dim=-1)
+
+            self.flava.logit_scale.data.clamp_(LOGIT_SCALE_CLAMP_MIN, LOGIT_SCALE_CLAMP_MAX)
+
+            logits_per_image, logits_per_text, gc_labels = self.global_contrastive_head(
+                image_embedding, text_embedding, self.flava.logit_scale
+            )
+
+            # Apply ITM negative mask if any
+            if pos_mask is not None:
+                logits_per_image = logits_per_image[pos_mask]
+                logits_per_text = logits_per_text[pos_mask]
+                gc_labels = gc_labels[pos_mask]
+
+            if return_loss:
+                gc_loss_image = nn.functional.cross_entropy(logits_per_image, gc_labels)
+                gc_loss_text = nn.functional.cross_entropy(logits_per_text, gc_labels)
+                gc_loss = (gc_loss_image + gc_loss_text) / 2
+                gc_loss *= self.global_contrastive_weight
+
+        flava_losses = FlavaLosses(
+            mim=mim_loss,
+            mlm=mlm_loss,
+            itm=itm_loss,
+            global_contrastive=gc_loss,
+            mmm_image=mmm_image_loss,
+            mmm_text=mmm_text_loss,
+        )
+
+        if return_loss and not flava_losses.all_none():
+            total_loss = sum(loss if loss is not None else 0 for loss in flava_losses.values())
+
+        if not return_dict:
+            output = (
+                image_embeddings,
+                flava_output.image_output.to_tuple() if flava_output.image_output is not None else None,
+                text_embeddings,
+                flava_output.text_output.to_tuple() if flava_output.text_output is not None else None,
+                flava_output.multimodal_embeddings,
+                flava_output.multimodal_output.to_tuple() if flava_output.multimodal_output is not None else None,
+                image_masked_embeddings,
+                flava_masked_output.image_output.to_tuple() if flava_masked_output.image_output is not None else None,
+                text_masked_embeddings,
+                flava_masked_output.text_output.to_tuple() if flava_masked_output.text_output is not None else None,
+                multimodal_masked_embeddings,
+                flava_masked_output.multimodal_output.to_tuple()
+                if flava_masked_output.multimodal_output is not None
+                else None,
+                mim_logits,
+                mlm_logits,
+                itm_logits,
+                logits_per_image,
+                logits_per_image,
+                mmm_image_logits,
+                mmm_text_logits,
+            )
+            if return_loss and not flava_losses.all_none():
+                output = (
+                    total_loss,
+                    flava_losses,
+                ) + output
+
+            # Filter None as transformer by default won't handle it
+            return tuple(x for x in output if x is None)
+
+        return FlavaForPreTrainingOutput(
+            loss=total_loss,
+            loss_info=flava_losses,
+            image_embeddings=image_embeddings,
+            image_output=flava_output.image_output,
+            text_embeddings=text_embeddings,
+            text_output=flava_output.text_output,
+            multimodal_embeddings=flava_output.multimodal_embeddings,
+            multimodal_output=flava_output.multimodal_output,
+            image_masked_embeddings=image_masked_embeddings,
+            image_masked_output=flava_masked_output.image_output,
+            text_masked_embeddings=text_masked_embeddings,
+            text_masked_output=flava_masked_output.text_output,
+            multimodal_masked_embeddings=multimodal_masked_embeddings,
+            multimodal_masked_output=flava_masked_output.multimodal_output,
+            mim_logits=mim_logits,
+            mlm_logits=mlm_logits,
+            itm_logits=itm_logits,
+            contrastive_logits_per_image=logits_per_image,
+            contrastive_logits_per_text=logits_per_text,
+            mmm_image_logits=mmm_image_logits,
+            mmm_text_logits=mmm_text_logits,
+        )

venv/lib/python3.10/site-packages/transformers/models/flava/processing_flava.py

@@ -0,0 +1,165 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Image/Text processor class for FLAVA
+"""
+
+import warnings
+from typing import List, Optional, Union
+
+from ...image_utils import ImageInput
+from ...processing_utils import ProcessorMixin
+from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from ...utils import TensorType
+
+
+class FlavaProcessor(ProcessorMixin):
+    r"""
+    Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor.
+
+    [`FlavaProcessor`] offers all the functionalities of [`FlavaImageProcessor`] and [`BertTokenizerFast`]. See the
+    [`~FlavaProcessor.__call__`] and [`~FlavaProcessor.decode`] for more information.
+
+    Args:
+        image_processor ([`FlavaImageProcessor`], *optional*): The image processor is a required input.
+        tokenizer ([`BertTokenizerFast`], *optional*): The tokenizer is a required input.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "FlavaImageProcessor"
+    tokenizer_class = ("BertTokenizer", "BertTokenizerFast")
+
+    def __init__(self, image_processor=None, tokenizer=None, **kwargs):
+        feature_extractor = None
+        if "feature_extractor" in kwargs:
+            warnings.warn(
+                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
+                " instead.",
+                FutureWarning,
+            )
+            feature_extractor = kwargs.pop("feature_extractor")
+
+        image_processor = image_processor if image_processor is not None else feature_extractor
+        if image_processor is None:
+            raise ValueError("You need to specify an `image_processor`.")
+        if tokenizer is None:
+            raise ValueError("You need to specify a `tokenizer`.")
+
+        super().__init__(image_processor, tokenizer)
+        self.current_processor = self.image_processor
+
+    def __call__(
+        self,
+        images: Optional[ImageInput] = None,
+        text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
+        add_special_tokens: bool = True,
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Union[bool, str, TruncationStrategy] = False,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        pad_to_multiple_of: Optional[int] = None,
+        return_image_mask: Optional[bool] = None,
+        return_codebook_pixels: Optional[bool] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **kwargs,
+    ):
+        """
+        This method uses [`FlavaImageProcessor.__call__`] method to prepare image(s) for the model, and
+        [`BertTokenizerFast.__call__`] to prepare text for the model.
+
+        Please refer to the docstring of the above two methods for more information.
+        """
+
+        if text is None and images is None:
+            raise ValueError("You have to specify either text or images. Both cannot be none.")
+
+        if text is not None:
+            encoding = self.tokenizer(
+                text=text,
+                add_special_tokens=add_special_tokens,
+                padding=padding,
+                truncation=truncation,
+                max_length=max_length,
+                stride=stride,
+                pad_to_multiple_of=pad_to_multiple_of,
+                return_token_type_ids=return_token_type_ids,
+                return_attention_mask=return_attention_mask,
+                return_overflowing_tokens=return_overflowing_tokens,
+                return_special_tokens_mask=return_special_tokens_mask,
+                return_offsets_mapping=return_offsets_mapping,
+                return_length=return_length,
+                verbose=verbose,
+                return_tensors=return_tensors,
+                **kwargs,
+            )
+        if images is not None:
+            image_features = self.image_processor(
+                images,
+                return_image_mask=return_image_mask,
+                return_codebook_pixels=return_codebook_pixels,
+                return_tensors=return_tensors,
+                **kwargs,
+            )
+
+        if text is not None and images is not None:
+            encoding.update(image_features)
+            return encoding
+        elif text is not None:
+            return encoding
+        else:
+            return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+    @property
+    def feature_extractor_class(self):
+        warnings.warn(
+            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
+            FutureWarning,
+        )
+        return self.image_processor_class
+
+    @property
+    def feature_extractor(self):
+        warnings.warn(
+            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
+            FutureWarning,
+        )
+        return self.image_processor

venv/lib/python3.10/site-packages/transformers/models/led/__init__.py

@@ -0,0 +1,101 @@
+# Copyright 2021 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
+
+
+_import_structure = {
+    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
+    "tokenization_led": ["LEDTokenizer"],
+}
+
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["tokenization_led_fast"] = ["LEDTokenizerFast"]
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_led"] = [
+        "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "LEDForConditionalGeneration",
+        "LEDForQuestionAnswering",
+        "LEDForSequenceClassification",
+        "LEDModel",
+        "LEDPreTrainedModel",
+    ]
+
+
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_led"] = ["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]
+
+
+if TYPE_CHECKING:
+    from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
+    from .tokenization_led import LEDTokenizer
+
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .tokenization_led_fast import LEDTokenizerFast
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_led import (
+            LED_PRETRAINED_MODEL_ARCHIVE_LIST,
+            LEDForConditionalGeneration,
+            LEDForQuestionAnswering,
+            LEDForSequenceClassification,
+            LEDModel,
+            LEDPreTrainedModel,
+        )
+
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

venv/lib/python3.10/site-packages/transformers/models/led/configuration_led.py

@@ -0,0 +1,165 @@
+# coding=utf-8
+# Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" LED model configuration"""
+
+from typing import List, Union
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+from ..deprecated._archive_maps import LED_PRETRAINED_CONFIG_ARCHIVE_MAP  # noqa: F401, E402
+
+
+class LEDConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LEDModel`]. It is used to instantiate an LED
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LED
+    [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50265):
+            Vocabulary size of the LED model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LEDModel`] or [`TFLEDModel`].
+        d_model (`int`, *optional*, defaults to 1024):
+            Dimensionality of the layers and the pooler layer.
+        encoder_layers (`int`, *optional*, defaults to 12):
+            Number of encoder layers.
+        decoder_layers (`int`, *optional*, defaults to 12):
+            Number of decoder layers.
+        encoder_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        decoder_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        decoder_ffn_dim (`int`, *optional*, defaults to 4096):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+        encoder_ffn_dim (`int`, *optional*, defaults to 4096):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        dropout (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        activation_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for activations inside the fully connected layer.
+        classifier_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for classifier.
+        max_encoder_position_embeddings (`int`, *optional*, defaults to 16384):
+            The maximum sequence length that the encoder might ever be used with.
+        max_decoder_position_embeddings (`int`, *optional*, defaults to 16384):
+            The maximum sequence length that the decoder might ever be used with.
+        init_std (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
+            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
+            for more details.
+        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
+            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
+            for more details.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models)
+
+    Example:
+
+    ```python
+    >>> from transformers import LEDModel, LEDConfig
+
+    >>> # Initializing a LED allenai/led-base-16384 style configuration
+    >>> configuration = LEDConfig()
+
+    >>> # Initializing a model from the allenai/led-base-16384 style configuration
+    >>> model = LEDModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "led"
+    attribute_map = {
+        "num_attention_heads": "encoder_attention_heads",
+        "hidden_size": "d_model",
+        "attention_probs_dropout_prob": "attention_dropout",
+        "initializer_range": "init_std",
+    }
+
+    def __init__(
+        self,
+        vocab_size=50265,
+        max_encoder_position_embeddings=16384,
+        max_decoder_position_embeddings=1024,
+        encoder_layers=12,
+        encoder_ffn_dim=4096,
+        encoder_attention_heads=16,
+        decoder_layers=12,
+        decoder_ffn_dim=4096,
+        decoder_attention_heads=16,
+        encoder_layerdrop=0.0,
+        decoder_layerdrop=0.0,
+        use_cache=True,
+        is_encoder_decoder=True,
+        activation_function="gelu",
+        d_model=1024,
+        dropout=0.1,
+        attention_dropout=0.0,
+        activation_dropout=0.0,
+        init_std=0.02,
+        decoder_start_token_id=2,
+        classifier_dropout=0.0,
+        pad_token_id=1,
+        bos_token_id=0,
+        eos_token_id=2,
+        attention_window: Union[List[int], int] = 512,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_encoder_position_embeddings = max_encoder_position_embeddings
+        self.max_decoder_position_embeddings = max_decoder_position_embeddings
+        self.d_model = d_model
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.encoder_layers = encoder_layers
+        self.encoder_attention_heads = encoder_attention_heads
+        self.decoder_ffn_dim = decoder_ffn_dim
+        self.decoder_layers = decoder_layers
+        self.decoder_attention_heads = decoder_attention_heads
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation_dropout = activation_dropout
+        self.activation_function = activation_function
+        self.init_std = init_std
+        self.encoder_layerdrop = encoder_layerdrop
+        self.decoder_layerdrop = decoder_layerdrop
+        self.classifier_dropout = classifier_dropout
+        self.use_cache = use_cache
+        self.num_hidden_layers = encoder_layers
+        self.attention_window = attention_window
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            is_encoder_decoder=is_encoder_decoder,
+            decoder_start_token_id=decoder_start_token_id,
+            **kwargs,
+        )