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ckpts/universal/global_step20/zero/21.mlp.dense_h_to_4h.weight/exp_avg_sq.pt

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ckpts/universal/global_step20/zero/21.mlp.dense_h_to_4h.weight/fp32.pt

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ckpts/universal/global_step20/zero/22.attention.query_key_value.weight/exp_avg.pt

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ckpts/universal/global_step20/zero/22.attention.query_key_value.weight/exp_avg_sq.pt

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ckpts/universal/global_step20/zero/22.attention.query_key_value.weight/fp32.pt

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lm-evaluation-harness/lm_eval/tasks/kmmlu/cot_hard/kmmlu_cot_hard_marketing.yaml

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+dataset_name: marketing
+doc_to_text: '질문: e-CRM상에서 고객유지를 위한 전략으로 맞지 않는 것은?
+
+  A.주문에 대한 신속하고 정확한 대응
+
+  B.품질이 낮아도 저렴한 제품의 대량공급
+
+  C.배송 등 철저한 사후관리
+
+  D.기술적인 지원체제 구축
+
+  정답: 차근 차근 생각해봅시다. 마케팅 관련 정보를 위해 위키피디아를 참조하겠습니다. e-CRM(Electronic Customer Relationship
+  Management)은 인터넷을 기반으로 한 고객관계관리를 의미합니다. 고객 유지를 위한 전략은 고객 만족도를 높이고 이탈을 방지하는 것이 목적입니다.
+  선택지를 하나씩 살펴보겠습니다. (A). 주문에 대한 신속하고 정확한 대응: 고객이 주문한 제품이나 서비스를 빠르게 제공하고 정확하게 처리하는
+  것은 고객 만족도를 높일 수 있습니다.  , 이는 고객 유지를 위한 전략으로 적합합니다. (B). 품질이 낮아도 저렴한 제품의 대량공급: 고객
+  만족도를 높이기 위해서는 제품의 품질이 중요합니다. 저렴한 제품을 대량 공급하는 것은 단기적으로는 고객을 유치할 수 있지만, 장기적으로는 고객
+  이탈을 유발할 수 있습니다.  , 이는 고객 유지를 위한 전략으로 적합하지 않습니다. (C). 배송 등 철저한 사후관리: 제품을 배송한 후에도
+  고객의 요구에 적극적으로 대응하고, 문제가 발생한 경우 신속하게 해결하는 것은 고객 만족도를 높일 수 있습니다.  , 이는 고객 유지를 위한 전략으로
+  적합합니다. (D). 기술적인 지원체제 구축: 고객이 제품이나 서비스를 이용하는 데 있어서 기술적인 문제가 발생한 경우, 이를 해결할 수 있는
+  지원체제를 구축하는 것은 고객 만족도를 높일 수 있습니다.  , 이는 고객 유지를 위한 전략으로 적합합니다. 따라서, 정답은 (B) 입니다.
+
+
+  질문: 데이터베이스 마케팅에서 사용되는 RFM의 세 가지 기준이 아닌 것은?
+
+  A.얼마나 오랫동안 우리와 거래했는가?
+
+  B.얼마나 최근에 우리 제품을 구매했는가?
+
+  C.얼마나 자주 우리 제품을 구입하는가?
+
+  D.우리 제품의 구입에 어느 정도의 돈을 쓰는가?
+
+  정답: 차근 차근 생각해봅시다. 마케팅 관련 정보를 위해 위키피디아를 참조하겠습니다. RFM은 데이터베이스 마케팅에서 중요한 개념으로, 고객의
+  가치를 평가하는 데 사용되는 세 가지 기준을 나타냅니다. RFM은 Recency(최근성), Frequency(빈도), Monetary Value(금액)의
+  약자입니다. (A) "얼마나 오랫동안 우리와 거래했는가?"는 고객이 얼마나 오래된 고객인지를 나타내는 지표이지만, RFM 모델에서는 직접적으로
+  고려되지 않습니다. RFM 모델은 최근에 고객이 얼마나 활동적이었는지(Recency), 고객이 얼마나 자주 구매했는지(Frequency), 그리고
+  고객이 얼마나 많은 돈을 썼는지(Monetary Value)를 중점적으로 보는 모델입니다. (B) "얼마나 최근에 우리 제품을 구매했는가?"는
+  RFM 모델의 R인 Recency를 나타냅니다. 이는 고객이 가장 최근에 구매한 시점을 의미하며, 이는 고객의 최근 구매 행동을 반영합니다. (C)
+  "얼마나 자주 우리 제품을 구입하는가?"는 RFM 모델의 F인 Frequency를 나타냅니다. 이는 고객이 얼마나 자주 구매하는지를 의미하며,
+  이는 고객의 구매 빈도를 반영합니다. (D) "우리 제품의 구입에 어느 정도의 돈을 쓰는가?"는 RFM 모델의 M인 Monetary Value를
+  나타냅니다. 이는 고객이 얼마나 많은 돈을 소비했는지를 의미하며, 이는 고객의 구매력을 반영합니다.  , RFM 모델에서 직접적으로 고려되지 않는
+  것은 (A) "얼마나 오랫동안 우리와 거래했는가?"입니다. 이는 고객의 거래 기간을 나타내지만, RFM 모델은 최근성, 빈도, 금액에 초점을 맞추고
+  있습니다. 따라서, 정답은 (A) 입니다.
+
+
+  질문: 아웃바운드 텔레마케팅에서 잠재고객을 구매고객으로 전환시키는 방법으로 볼 수 없는 것은?
+
+  A.고객을 이해시키고 실질적 혜택 부여
+
+  B.무조건 가격할인을 통한 유도
+
+  C.관심이 많은 고객을 집중적으로 설득
+
+  D.쌍방간 커뮤니케이션 강화
+
+  정답: 차근 차근 생각해봅시다. 마케팅 관련 정보를 위해 위키피디아를 참조하겠습니다. 아웃바운드 텔레마케팅은 기업이 직접 고객에게 연락하여 제품이나
+  서비스를 판매하는 방식을 말합니다. 이때, 잠재 고객을 구매 고객으로 전환시키는 방법에는 여러 가지가 있습니다. (A) 고객을 이해시키고 실질적
+  혜택 부여는 잠재 고객을 구매 고객으로 전환시키는 중요한 방법 중 하나입니다. 고객이 제품��나 서비스에 대한 이해를 가지고, 그것이 자신에게 어떤
+  실질적인 혜택을 줄 수 있는지를 알게 되면, 구매로 이어질 가능성이 높아집니다. (C) 관심이 많은 고객을 집중적으로 설득하는 것도 잠재 고객을
+  구매 고객으로 전환시키는 효과적인 방법입니다. 이미 제품이나 서비스에 대한 관심이 있는 고객을 대상으로 하면, 그들을 구매로 이끌 가능성이 더욱
+  높아집니다. (D) 쌍방간 커뮤니케이션 강화 역시 잠재 고객을 구매 고객으로 전환시키는 데 중요한 역할을 합니다. 고객과의 커뮤니케이션을 통해
+  고객의 필요와 원하는 것을 파악하고, 그에 맞는 제품이나 서비스를 제공하면 고객의 구매 결정을 유도할 수 있습니다. 그러나, (B) 무조건 가격할인을
+  통한 유도는 잠재 고객을 구매 고객으로 전환시키는 방법으로 적절하지 않을 수 있습니다. 가격할인은 단기적으로는 고객의 구매를 유도할 수 있지만,
+  장기적으로는 기업의 이익을 해칠 수 있으며, 고객이 제품이나 서비스의 진정한 가치를 인지하지 못하게 만들 수 있습니다. 또한, 가격만으로 고객을
+  유도하는 것은 고객의 브랜드 충성도를 낮추고, 가격 경쟁에 빠져 기업의 경쟁력을 약화시킬 수 있습니다. 따라서, 정답은 (B) 입니다.
+
+
+  질문: 회사가 제품에 대한 가격을 결정할 때 제품의 저가전략이 적합한 경우가 아닌 것은?
+
+  A.시장수요의 가격탄력성이 낮을 때
+
+  B.경쟁기업에 비해 원가우위를 확보하고 있을 때
+
+  C.경쟁사가 많을 때
+
+  D.소비자들의 수요를 자극하고자 할 때
+
+  정답: 차근 차근 생각해봅시다. 마케팅 관련 정보를 위해 위키피디아를 참조하겠습니다. 가장 먼저 저가전략이란 것은 회사가 제품의 가격을 낮추어
+  소비자들의 수요를 끌어들이거나, 경쟁사에 대한 가격 경쟁력을 확보하기 위해 사용하는 전략입니다. (A) 선택지를 보면, ''시장수요의 가격탄력성이
+  낮을 때''라고 되어 있습니다. 가격탄력성이란 가격 변동에 따른 수요 변동률을 의미하는데, 이 값이 낮다는 것은 가격이 변해도 소비자들의 수요에
+  크게 영향을 주지 않는다는 것을 의미합니다. 즉, 가격을 낮춰도 소비자들의 수요가 크게 증가하지 않을 것이므로, 이 경우 저가전략을 사용하는 것은
+  적합하지 않을 수 있습니다. (B) 선택지인 ''경쟁기업에 비해 원가우위를 확보하고 있을 때''는 저가전략을 사용하기 좋은 상황입니다. 원가우위를
+  확보하고 있다는 것은 동일한 제품을 더 낮은 비용으로 생산할 수 있다는 것을 의미하므로, 이 경우 회사는 가격을 낮추어도 이익을 낼 수 있습니다.   이
+  경우 저가전략이 적합합니다. (C) 선택지인 ''경쟁사가 많을 때''도 저가전략을 사용하기 좋은 상황입니다. 경쟁사가 많을 경우 소비자들의 선택권이
+  넓어지므로, 가격 경쟁력을 확보하기 위해 저가전략을 사용할 수 있습니다. (D) 선택지인 ''소비자들의 수요를 자극하고자 할 때''는 저가전략의
+  주요 목표 중 하나입니다. 가격을 낮추면 소비자들의 구매욕구를 자극할 수 있으므로, 이 경우에도 저가전략이 적합합니다.  , 이러한 사항들을 종합해
+  보았을 때, 저가전략이 적합하지 않은 경우는 ''시장수요의 가격탄력성이 낮을 때''입니다. 이 경우에는 가격을 낮추어도 소비자들의 수요에 큰 영향을
+  미치지 못하므로, 저가전략을 사용하는 것은 효과적이지 않을 수 있습니다. 따라서, 정답은 (A) 입니다.
+
+
+  질문: 아웃바운드 텔레마케팅의 판매촉진 강화를 위한 방안으로 잘못된 것은?
+
+  A.상담원은 고객의 요구만을 열심히 경청하게 한다.
+
+  B.상담원들에게 상품에 대한 사전지식을 철저히 준비토록 한다.
+
+  C.고객에게 호감을 줄 수 있는 커뮤니케이션 기술을 갖추도록 한다.
+
+  D.상담원은 고객의 반론에 대한 자연스러운 대응력을 갖추도록 한다.
+
+  정답: 차근 차근 생각해봅시다. 마케팅 관련 정보를 위해 위키피디아를 참조하겠습니다. 정답 해설: 아웃바운드 텔레마케팅은 고객에게 먼저 전화를
+  걸어 상품을 판매하거나 서비스를 제공하는 마케팅 방식입니다.  , 판매촉진 강화를 위해서는 상담원이 적극적으로 고객을 설득하고, 상품에 대한 정보를
+  제공하는 것이 중요합니다. (A)는 고객의 요구만을 열심히 경청하게 한다는 것은 아웃바운드 텔레마케팅의 목적인 판매촉진 강화에 부합하지 않습니다.
+  이는 고객의 요구를 파악하는 데에는 도움이 될 수 있지만, ���매를 이끌어내는 데에는 한계가 있습니다. (B), (C), (D)는 모두 아웃바운드
+  텔레마케팅의 판매촉진 강화를 위한 방안으로 적절한 것들입니다. 상담원이 상품에 대한 사전지식을 철저히 준비하면 고객에게 정확한 정보를 제공할 수
+  있고, 고객에게 호감을 줄 수 있는 커뮤니케이션 기술을 갖추면 고객과의 소통을 원활하게 할 수 있으며, 상담원이 고객의 반론에 대한 자연스러운
+  대응력을 갖추면 고객의 불만이나 반대를 효과적으로 처리할 수 있습니다. 따라서, 정답은 (A) 입니다.
+
+
+  질문: {{question}}
+
+  A.{{A}}
+
+  B.{{B}}
+
+  C.{{C}}
+
+  D.{{D}}
+
+  정답: 차근 차근 생각해봅시다. 마케팅 관련 정보를 위해 위키피디아를 참조하겠습니다.'
+include: _cot_kmmlu_yaml
+task: kmmlu_hard_cot_marketing

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/_hard_kmmlu_yaml

@@ -0,0 +1,19 @@
+group:
+    - kmmlu
+    - kmmlu_hard
+dataset_path: HAERAE-HUB/KMMLU-HARD
+output_type: multiple_choice
+test_split: test
+fewshot_split: dev
+doc_to_text: "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n정답："
+doc_to_choice: ["A", "B", "C", "D"]
+doc_to_target: "{{answer-1}}"
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 2.0

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_accounting.yaml

@@ -0,0 +1,3 @@
+dataset_name: accounting
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_accounting

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_agricultural_sciences.yaml

@@ -0,0 +1,3 @@
+dataset_name: agricultural_sciences
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_agricultural_sciences

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_biology.yaml

@@ -0,0 +1,3 @@
+dataset_name: biology
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_biology

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_computer_science.yaml

@@ -0,0 +1,3 @@
+dataset_name: computer_science
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_computer_science

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_construction.yaml

@@ -0,0 +1,3 @@
+dataset_name: construction
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_construction

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_economics.yaml

@@ -0,0 +1,3 @@
+dataset_name: economics
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_economics

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_education.yaml

@@ -0,0 +1,3 @@
+dataset_name: education
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_education

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_energy_management.yaml

@@ -0,0 +1,3 @@
+dataset_name: energy_management
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_energy_management

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_gas_technology_and_engineering.yaml

@@ -0,0 +1,3 @@
+dataset_name: gas_technology_and_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_gas_technology_and_engineering

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_health.yaml

@@ -0,0 +1,3 @@
+dataset_name: health
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_health

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_information_technology.yaml

@@ -0,0 +1,3 @@
+dataset_name: information_technology
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_information_technology

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_interior_architecture_and_design.yaml

@@ -0,0 +1,3 @@
+dataset_name: interior_architecture_and_design
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_interior_architecture_and_design

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_korean_history.yaml

@@ -0,0 +1,3 @@
+dataset_name: korean_history
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_korean_history

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_law.yaml

@@ -0,0 +1,3 @@
+dataset_name: law
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_law

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_machine_design_and_manufacturing.yaml

@@ -0,0 +1,3 @@
+dataset_name: machine_design_and_manufacturing
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_machine_design_and_manufacturing

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_math.yaml

@@ -0,0 +1,3 @@
+dataset_name: math
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_math

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_mechanical_engineering.yaml

@@ -0,0 +1,3 @@
+dataset_name: mechanical_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_mechanical_engineering

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_public_safety.yaml

@@ -0,0 +1,3 @@
+dataset_name: public_safety
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_public_safety

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_refrigerating_machinery.yaml

@@ -0,0 +1,3 @@
+dataset_name: refrigerating_machinery
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_refrigerating_machinery

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_social_welfare.yaml

@@ -0,0 +1,3 @@
+dataset_name: social_welfare
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_social_welfare

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_taxation.yaml

@@ -0,0 +1,3 @@
+dataset_name: taxation
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_taxation

lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_telecommunications_and_wireless_technology.yaml

@@ -0,0 +1,3 @@
+dataset_name: telecommunications_and_wireless_technology
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_telecommunications_and_wireless_technology

venv/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

venv/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/LICENSE

@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2016 Tsuyoshi Hombashi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

venv/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/METADATA

@@ -0,0 +1,276 @@
+Metadata-Version: 2.1
+Name: DataProperty
+Version: 1.0.1
+Summary: Python library for extract property from data.
+Home-page: https://github.com/thombashi/DataProperty
+Author: Tsuyoshi Hombashi
+Author-email: [email protected]
+Maintainer: Tsuyoshi Hombashi
+Maintainer-email: [email protected]
+License: MIT License
+Project-URL: Source, https://github.com/thombashi/DataProperty
+Project-URL: Tracker, https://github.com/thombashi/DataProperty/issues
+Keywords: data,library,property
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Information Technology
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: mbstrdecoder (<2,>=1.0.0)
+Requires-Dist: typepy[datetime] (<2,>=1.2.0)
+Provides-Extra: logging
+Requires-Dist: loguru (<1,>=0.4.1) ; extra == 'logging'
+Provides-Extra: test
+Requires-Dist: pytest (>=6.0.1) ; extra == 'test'
+Requires-Dist: pytest-md-report (>=0.3) ; extra == 'test'
+Requires-Dist: tcolorpy (>=0.1.2) ; extra == 'test'
+
+.. contents:: **DataProperty**
+   :backlinks: top
+   :local:
+
+
+Summary
+=======
+A Python library for extract property from data.
+
+
+.. image:: https://badge.fury.io/py/DataProperty.svg
+    :target: https://badge.fury.io/py/DataProperty
+    :alt: PyPI package version
+
+.. image:: https://anaconda.org/conda-forge/DataProperty/badges/version.svg
+    :target: https://anaconda.org/conda-forge/DataProperty
+    :alt: conda-forge package version
+
+.. image:: https://img.shields.io/pypi/pyversions/DataProperty.svg
+   :target: https://pypi.org/project/DataProperty
+    :alt: Supported Python versions
+
+.. image:: https://img.shields.io/pypi/implementation/DataProperty.svg
+    :target: https://pypi.org/project/DataProperty
+    :alt: Supported Python implementations
+
+.. image:: https://github.com/thombashi/DataProperty/actions/workflows/ci.yml/badge.svg
+    :target: https://github.com/thombashi/DataProperty/actions/workflows/ci.yml
+    :alt: CI status of Linux/macOS/Windows
+
+.. image:: https://coveralls.io/repos/github/thombashi/DataProperty/badge.svg?branch=master
+    :target: https://coveralls.io/github/thombashi/DataProperty?branch=master
+    :alt: Test coverage
+
+.. image:: https://github.com/thombashi/DataProperty/actions/workflows/github-code-scanning/codeql/badge.svg
+    :target: https://github.com/thombashi/DataProperty/actions/workflows/github-code-scanning/codeql
+    :alt: CodeQL
+
+
+Installation
+============
+
+Installation: pip
+------------------------------
+::
+
+    pip install DataProperty
+
+Installation: conda
+------------------------------
+::
+
+    conda install -c conda-forge dataproperty
+
+Installation: apt
+------------------------------
+::
+
+    sudo add-apt-repository ppa:thombashi/ppa
+    sudo apt update
+    sudo apt install python3-dataproperty
+
+
+Usage
+=====
+
+Extract property of data
+------------------------
+
+e.g. Extract a ``float`` value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> from dataproperty import DataProperty
+    >>> DataProperty(-1.1)
+    data=-1.1, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=1, extra_len=1
+
+e.g. Extract a ``int`` value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> from dataproperty import DataProperty
+    >>> DataProperty(123456789)
+    data=123456789, type=INTEGER, align=right, ascii_width=9, int_digits=9, decimal_places=0, extra_len=0
+
+e.g. Extract a ``str`` (ascii) value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> from dataproperty import DataProperty
+    >>> DataProperty("sample string")
+    data=sample string, type=STRING, align=left, length=13, ascii_width=13, extra_len=0
+
+e.g. Extract a ``str`` (multi-byte) value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> from dataproperty import DataProperty
+    >>> str(DataProperty("吾輩は猫である"))
+    data=吾輩は猫である, type=STRING, align=left, length=7, ascii_width=14, extra_len=0
+
+e.g. Extract a time (``datetime``) value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> import datetime
+    >>> from dataproperty import DataProperty
+    >>> DataProperty(datetime.datetime(2017, 1, 1, 0, 0, 0))
+    data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0
+
+e.g. Extract a ``bool`` value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> from dataproperty import DataProperty
+    >>> DataProperty(True)
+    data=True, type=BOOL, align=left, ascii_width=4, extra_len=0
+
+
+Extract data property for each element from a matrix
+----------------------------------------------------
+``DataPropertyExtractor.to_dp_matrix`` method returns a matrix of ``DataProperty`` instances from a data matrix.
+An example data set and the result are as follows:
+
+:Sample Code:
+    .. code:: python
+
+        import datetime
+        from dataproperty import DataPropertyExtractor
+
+        dp_extractor = DataPropertyExtractor()
+        dt = datetime.datetime(2017, 1, 1, 0, 0, 0)
+        inf = float("inf")
+        nan = float("nan")
+
+        dp_matrix = dp_extractor.to_dp_matrix([
+            [1, 1.1, "aa", 1, 1, True, inf, nan, dt],
+            [2, 2.2, "bbb", 2.2, 2.2, False, "inf", "nan", dt],
+            [3, 3.33, "cccc", -3, "ccc", "true", inf, "NAN", "2017-01-01T01:23:45+0900"],
+        ])
+
+        for row, dp_list in enumerate(dp_matrix):
+            for col, dp in enumerate(dp_list):
+                print("row={:d}, col={:d}, {}".format(row, col, str(dp)))
+
+:Output:
+    ::
+
+        row=0, col=0, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
+        row=0, col=1, data=1.1, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
+        row=0, col=2, data=aa, type=STRING, align=left, ascii_width=2, length=2, extra_len=0
+        row=0, col=3, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
+        row=0, col=4, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
+        row=0, col=5, data=True, type=BOOL, align=left, ascii_width=4, extra_len=0
+        row=0, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0
+        row=0, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0
+        row=0, col=8, data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0
+        row=1, col=0, data=2, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
+        row=1, col=1, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
+        row=1, col=2, data=bbb, type=STRING, align=left, ascii_width=3, length=3, extra_len=0
+        row=1, col=3, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
+        row=1, col=4, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
+        row=1, col=5, data=False, type=BOOL, align=left, ascii_width=5, extra_len=0
+        row=1, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0
+        row=1, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0
+        row=1, col=8, data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0
+        row=2, col=0, data=3, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
+        row=2, col=1, data=3.33, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=2, extra_len=0
+        row=2, col=2, data=cccc, type=STRING, align=left, ascii_width=4, length=4, extra_len=0
+        row=2, col=3, data=-3, type=INTEGER, align=right, ascii_width=2, int_digits=1, decimal_places=0, extra_len=1
+        row=2, col=4, data=ccc, type=STRING, align=left, ascii_width=3, length=3, extra_len=0
+        row=2, col=5, data=True, type=BOOL, align=left, ascii_width=4, extra_len=0
+        row=2, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0
+        row=2, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0
+        row=2, col=8, data=2017-01-01T01:23:45+0900, type=STRING, align=left, ascii_width=24, length=24, extra_len=0
+
+
+Full example source code can be found at *examples/py/to_dp_matrix.py*
+
+
+Extract properties for each column from a matrix
+------------------------------------------------------
+``DataPropertyExtractor.to_column_dp_list`` method returns a list of ``DataProperty`` instances from a data matrix. The list represents the properties for each column.
+An example data set and the result are as follows:
+
+Example data set and result are as follows:
+
+:Sample Code:
+    .. code:: python
+
+        import datetime
+        from dataproperty import DataPropertyExtractor
+
+        dp_extractor = DataPropertyExtractor()
+        dt = datetime.datetime(2017, 1, 1, 0, 0, 0)
+        inf = float("inf")
+        nan = float("nan")
+
+        data_matrix = [
+            [1, 1.1,  "aa",   1,   1,     True,   inf,   nan,   dt],
+            [2, 2.2,  "bbb",  2.2, 2.2,   False,  "inf", "nan", dt],
+            [3, 3.33, "cccc", -3,  "ccc", "true", inf,   "NAN", "2017-01-01T01:23:45+0900"],
+        ]
+
+        dp_extractor.headers = ["int", "float", "str", "num", "mix", "bool", "inf", "nan", "time"]
+        col_dp_list = dp_extractor.to_column_dp_list(dp_extractor.to_dp_matrix(dp_matrix))
+
+        for col_idx, col_dp in enumerate(col_dp_list):
+            print(str(col_dp))
+
+:Output:
+    ::
+
+        column=0, type=INTEGER, align=right, ascii_width=3, bit_len=2, int_digits=1, decimal_places=0
+        column=1, type=REAL_NUMBER, align=right, ascii_width=5, int_digits=1, decimal_places=(min=1, max=2)
+        column=2, type=STRING, align=left, ascii_width=4
+        column=3, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=(min=0, max=1), extra_len=(min=0, max=1)
+        column=4, type=STRING, align=left, ascii_width=3, int_digits=1, decimal_places=(min=0, max=1)
+        column=5, type=BOOL, align=left, ascii_width=5
+        column=6, type=INFINITY, align=left, ascii_width=8
+        column=7, type=NAN, align=left, ascii_width=3
+        column=8, type=STRING, align=left, ascii_width=24
+
+
+Full example source code can be found at *examples/py/to_column_dp_list.py*
+
+
+Dependencies
+============
+- Python 3.7+
+- `Python package dependencies (automatically installed) <https://github.com/thombashi/DataProperty/network/dependencies>`__
+
+Optional dependencies
+---------------------
+- `loguru <https://github.com/Delgan/loguru>`__
+    - Used for logging if the package installed

venv/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/RECORD

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+dataproperty/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+dataproperty/typing.py,sha256=YhjN4wF_7uqG9tPUbFLFemWIzx3WgyJJFhTh62TyhJU,1403

venv/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.40.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

venv/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/top_level.txt

@@ -0,0 +1 @@
+dataproperty

venv/lib/python3.10/site-packages/_distutils_hack/__init__.py

@@ -0,0 +1,132 @@
+import sys
+import os
+import re
+import importlib
+import warnings
+
+
+is_pypy = '__pypy__' in sys.builtin_module_names
+
+
+warnings.filterwarnings('ignore',
+                        r'.+ distutils\b.+ deprecated',
+                        DeprecationWarning)
+
+
+def warn_distutils_present():
+    if 'distutils' not in sys.modules:
+        return
+    if is_pypy and sys.version_info < (3, 7):
+        # PyPy for 3.6 unconditionally imports distutils, so bypass the warning
+        # https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
+        return
+    warnings.warn(
+        "Distutils was imported before Setuptools, but importing Setuptools "
+        "also replaces the `distutils` module in `sys.modules`. This may lead "
+        "to undesirable behaviors or errors. To avoid these issues, avoid "
+        "using distutils directly, ensure that setuptools is installed in the "
+        "traditional way (e.g. not an editable install), and/or make sure "
+        "that setuptools is always imported before distutils.")
+
+
+def clear_distutils():
+    if 'distutils' not in sys.modules:
+        return
+    warnings.warn("Setuptools is replacing distutils.")
+    mods = [name for name in sys.modules if re.match(r'distutils\b', name)]
+    for name in mods:
+        del sys.modules[name]
+
+
+def enabled():
+    """
+    Allow selection of distutils by environment variable.
+    """
+    which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'stdlib')
+    return which == 'local'
+
+
+def ensure_local_distutils():
+    clear_distutils()
+
+    # With the DistutilsMetaFinder in place,
+    # perform an import to cause distutils to be
+    # loaded from setuptools._distutils. Ref #2906.
+    add_shim()
+    importlib.import_module('distutils')
+    remove_shim()
+
+    # check that submodules load as expected
+    core = importlib.import_module('distutils.core')
+    assert '_distutils' in core.__file__, core.__file__
+
+
+def do_override():
+    """
+    Ensure that the local copy of distutils is preferred over stdlib.
+
+    See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
+    for more motivation.
+    """
+    if enabled():
+        warn_distutils_present()
+        ensure_local_distutils()
+
+
+class DistutilsMetaFinder:
+    def find_spec(self, fullname, path, target=None):
+        if path is not None:
+            return
+
+        method_name = 'spec_for_{fullname}'.format(**locals())
+        method = getattr(self, method_name, lambda: None)
+        return method()
+
+    def spec_for_distutils(self):
+        import importlib.abc
+        import importlib.util
+
+        class DistutilsLoader(importlib.abc.Loader):
+
+            def create_module(self, spec):
+                return importlib.import_module('setuptools._distutils')
+
+            def exec_module(self, module):
+                pass
+
+        return importlib.util.spec_from_loader('distutils', DistutilsLoader())
+
+    def spec_for_pip(self):
+        """
+        Ensure stdlib distutils when running under pip.
+        See pypa/pip#8761 for rationale.
+        """
+        if self.pip_imported_during_build():
+            return
+        clear_distutils()
+        self.spec_for_distutils = lambda: None
+
+    @staticmethod
+    def pip_imported_during_build():
+        """
+        Detect if pip is being imported in a build script. Ref #2355.
+        """
+        import traceback
+        return any(
+            frame.f_globals['__file__'].endswith('setup.py')
+            for frame, line in traceback.walk_stack(None)
+        )
+
+
+DISTUTILS_FINDER = DistutilsMetaFinder()
+
+
+def add_shim():
+    sys.meta_path.insert(0, DISTUTILS_FINDER)
+
+
+def remove_shim():
+    try:
+        sys.meta_path.remove(DISTUTILS_FINDER)
+    except ValueError:
+        pass

venv/lib/python3.10/site-packages/_distutils_hack/override.py

@@ -0,0 +1 @@
+__import__('_distutils_hack').do_override()

venv/lib/python3.10/site-packages/jsonlines/__init__.py

@@ -0,0 +1,20 @@
+"""
+Module for the jsonlines data format.
+"""
+
+# expose only public api
+from .jsonlines import (
+    Error,
+    InvalidLineError,
+    Reader,
+    Writer,
+    open,
+)
+
+__all__ = [
+    "Error",
+    "InvalidLineError",
+    "Reader",
+    "Writer",
+    "open",
+]

venv/lib/python3.10/site-packages/jsonlines/jsonlines.py

@@ -0,0 +1,665 @@
+"""
+jsonlines implementation
+"""
+
+import builtins
+import codecs
+import enum
+import io
+import json
+import os
+import types
+import typing
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    Iterator,
+    List,
+    Literal,
+    Optional,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+    overload,
+)
+
+import attr
+
+orjson: Optional[types.ModuleType]
+try:
+    import orjson
+except ImportError:
+    orjson = None
+
+ujson: Optional[types.ModuleType]
+try:
+    import ujson
+except ImportError:
+    ujson = None
+
+
+VALID_TYPES = {
+    bool,
+    dict,
+    float,
+    int,
+    list,
+    str,
+}
+
+# Characters to skip at the beginning of a line. Note: at most one such
+# character is skipped per line.
+SKIPPABLE_SINGLE_INITIAL_CHARS = (
+    "\x1e",  # RFC7464 text sequence
+    codecs.BOM_UTF8.decode(),
+)
+
+
+class DumpsResultConversion(enum.Enum):
+    LeaveAsIs = enum.auto()
+    EncodeToBytes = enum.auto()
+    DecodeToString = enum.auto()
+
+
+# https://docs.python.org/3/library/functions.html#open
+Openable = Union[str, bytes, int, os.PathLike]
+
+LoadsCallable = Callable[[Union[str, bytes]], Any]
+DumpsCallable = Callable[[Any], Union[str, bytes]]
+
+# Currently, JSON structures cannot be typed properly:
+# - https://github.com/python/typing/issues/182
+# - https://github.com/python/mypy/issues/731
+JSONCollection = Union[Dict[str, Any], List[Any]]
+JSONScalar = Union[bool, float, int, str]
+JSONValue = Union[JSONCollection, JSONScalar]
+TJSONValue = TypeVar("TJSONValue", bound=JSONValue)
+
+TRW = TypeVar("TRW", bound="ReaderWriterBase")
+
+# Default to using the fastest JSON library for reading, falling back to the
+# standard library (always available) if none are installed.
+if orjson is not None:
+    default_loads = orjson.loads
+elif ujson is not None:
+    default_loads = ujson.loads
+else:
+    default_loads = json.loads
+
+
+# For writing, use the stdlib. Other packages may be faster but their behaviour
+# (supported types etc.) and output (whitespace etc.) are not the same as the
+# stdlib json module, so this should be opt-in via the ‘dumps=’ arg.
+def default_dumps(obj: Any) -> str:
+    """
+    Fake ``dumps()`` function to use as a default marker.
+    """
+    raise NotImplementedError  # pragma: no cover
+
+
+@attr.s(auto_exc=True, auto_attribs=True)
+class Error(Exception):
+    """
+    Base error class.
+    """
+
+    message: str
+
+
+@attr.s(auto_exc=True, auto_attribs=True, init=False)
+class InvalidLineError(Error, ValueError):
+    """
+    Error raised when an invalid line is encountered.
+
+    This happens when the line does not contain valid JSON, or if a
+    specific data type has been requested, and the line contained a
+    different data type.
+
+    The original line itself is stored on the exception instance as the
+    ``.line`` attribute, and the line number as ``.lineno``.
+
+    This class subclasses both ``jsonlines.Error`` and the built-in
+    ``ValueError``.
+    """
+
+    #: The invalid line
+    line: Union[str, bytes]
+
+    #: The line number
+    lineno: int
+
+    def __init__(self, message: str, line: Union[str, bytes], lineno: int) -> None:
+        self.line = line.rstrip()
+        self.lineno = lineno
+        super().__init__(f"{message} (line {lineno})")
+
+
+@attr.s(auto_attribs=True, repr=False)
+class ReaderWriterBase:
+    """
+    Base class with shared behaviour for both the reader and writer.
+    """
+
+    _fp: Union[typing.IO[str], typing.IO[bytes], None] = attr.ib(
+        default=None, init=False
+    )
+    _closed: bool = attr.ib(default=False, init=False)
+    _should_close_fp: bool = attr.ib(default=False, init=False)
+
+    def close(self) -> None:
+        """
+        Close this reader/writer.
+
+        This closes the underlying file if that file has been opened by
+        this reader/writer. When an already opened file-like object was
+        provided, the caller is responsible for closing it.
+        """
+        if self._closed:
+            return
+        self._closed = True
+        if self._fp is not None and self._should_close_fp:
+            self._fp.close()
+
+    def __repr__(self) -> str:
+        cls_name = type(self).__name__
+        wrapped = self._repr_for_wrapped()
+        return f"<jsonlines.{cls_name} at 0x{id(self):x} wrapping {wrapped}>"
+
+    def _repr_for_wrapped(self) -> str:
+        raise NotImplementedError  # pragma: no cover
+
+    def __enter__(self: TRW) -> TRW:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: Optional[Type[BaseException]],
+        exc_val: Optional[BaseException],
+        exc_tb: Optional[types.TracebackType],
+    ) -> None:
+        self.close()
+
+
+@attr.s(auto_attribs=True, repr=False)
+class Reader(ReaderWriterBase):
+    """
+    Reader for the jsonlines format.
+
+    The first argument must be an iterable that yields JSON encoded
+    strings. Usually this will be a readable file-like object, such as
+    an open file or an ``io.TextIO`` instance, but it can also be
+    something else as long as it yields strings when iterated over.
+
+    Instances are iterable and can be used as a context manager.
+
+    The `loads` argument can be used to replace the standard json
+    decoder. If specified, it must be a callable that accepts a
+    (unicode) string and returns the decoded object.
+
+    :param file_or_iterable: file-like object or iterable yielding lines as
+        strings
+    :param loads: custom json decoder callable
+    """
+
+    _file_or_iterable: Union[
+        typing.IO[str], typing.IO[bytes], Iterable[Union[str, bytes]]
+    ]
+    _line_iter: Iterator[Tuple[int, Union[bytes, str]]] = attr.ib(init=False)
+    _loads: LoadsCallable = attr.ib(default=default_loads, kw_only=True)
+
+    def __attrs_post_init__(self) -> None:
+        if isinstance(self._file_or_iterable, io.IOBase):
+            self._fp = cast(
+                Union[typing.IO[str], typing.IO[bytes]],
+                self._file_or_iterable,
+            )
+
+        self._line_iter = enumerate(self._file_or_iterable, 1)
+
+    # No type specified, None not allowed
+    @overload
+    def read(
+        self,
+        *,
+        type: Literal[None] = ...,
+        allow_none: Literal[False] = ...,
+        skip_empty: bool = ...,
+    ) -> JSONValue:
+        ...  # pragma: no cover
+
+    # No type specified, None allowed
+    @overload
+    def read(
+        self,
+        *,
+        type: Literal[None] = ...,
+        allow_none: Literal[True],
+        skip_empty: bool = ...,
+    ) -> Optional[JSONValue]:
+        ...  # pragma: no cover
+
+    # Type specified, None not allowed
+    @overload
+    def read(
+        self,
+        *,
+        type: Type[TJSONValue],
+        allow_none: Literal[False] = ...,
+        skip_empty: bool = ...,
+    ) -> TJSONValue:
+        ...  # pragma: no cover
+
+    # Type specified, None allowed
+    @overload
+    def read(
+        self,
+        *,
+        type: Type[TJSONValue],
+        allow_none: Literal[True],
+        skip_empty: bool = ...,
+    ) -> Optional[TJSONValue]:
+        ...  # pragma: no cover
+
+    # Generic definition
+    @overload
+    def read(
+        self,
+        *,
+        type: Optional[Type[Any]] = ...,
+        allow_none: bool = ...,
+        skip_empty: bool = ...,
+    ) -> Optional[JSONValue]:
+        ...  # pragma: no cover
+
+    def read(
+        self,
+        *,
+        type: Optional[Type[Any]] = None,
+        allow_none: bool = False,
+        skip_empty: bool = False,
+    ) -> Optional[JSONValue]:
+        """
+        Read and decode a line.
+
+        The optional `type` argument specifies the expected data type.
+        Supported types are ``dict``, ``list``, ``str``, ``int``,
+        ``float``, and ``bool``. When specified, non-conforming lines
+        result in :py:exc:`InvalidLineError`.
+
+        By default, input lines containing ``null`` (in JSON) are
+        considered invalid, and will cause :py:exc:`InvalidLineError`.
+        The `allow_none` argument can be used to change this behaviour,
+        in which case ``None`` will be returned instead.
+
+        If `skip_empty` is set to ``True``, empty lines and lines
+        containing only whitespace are silently skipped.
+        """
+        if self._closed:
+            raise RuntimeError("reader is closed")
+        if type is not None and type not in VALID_TYPES:
+            raise ValueError("invalid type specified")
+
+        try:
+            lineno, line = next(self._line_iter)
+            while skip_empty and not line.rstrip():
+                lineno, line = next(self._line_iter)
+        except StopIteration:
+            raise EOFError from None
+
+        if isinstance(line, bytes):
+            try:
+                line = line.decode("utf-8")
+            except UnicodeDecodeError as orig_exc:
+                exc = InvalidLineError(
+                    f"line is not valid utf-8: {orig_exc}", line, lineno
+                )
+                raise exc from orig_exc
+
+        if line.startswith(SKIPPABLE_SINGLE_INITIAL_CHARS):
+            line = line[1:]
+
+        try:
+            value: JSONValue = self._loads(line)
+        except ValueError as orig_exc:
+            exc = InvalidLineError(
+                f"line contains invalid json: {orig_exc}", line, lineno
+            )
+            raise exc from orig_exc
+
+        if value is None:
+            if allow_none:
+                return None
+            raise InvalidLineError("line contains null value", line, lineno)
+
+        if type is not None:
+            valid = isinstance(value, type)
+            if type is int and isinstance(value, bool):
+                # isinstance() is not sufficient, since bool is an int subclass
+                valid = False
+            if not valid:
+                raise InvalidLineError(
+                    "line does not match requested type", line, lineno
+                )
+
+        return value
+
+    # No type specified, None not allowed
+    @overload
+    def iter(
+        self,
+        *,
+        type: Literal[None] = ...,
+        allow_none: Literal[False] = ...,
+        skip_empty: bool = ...,
+        skip_invalid: bool = ...,
+    ) -> Iterator[JSONValue]:
+        ...  # pragma: no cover
+
+    # No type specified, None allowed
+    @overload
+    def iter(
+        self,
+        *,
+        type: Literal[None] = ...,
+        allow_none: Literal[True],
+        skip_empty: bool = ...,
+        skip_invalid: bool = ...,
+    ) -> Iterator[JSONValue]:
+        ...  # pragma: no cover
+
+    # Type specified, None not allowed
+    @overload
+    def iter(
+        self,
+        *,
+        type: Type[TJSONValue],
+        allow_none: Literal[False] = ...,
+        skip_empty: bool = ...,
+        skip_invalid: bool = ...,
+    ) -> Iterator[TJSONValue]:
+        ...  # pragma: no cover
+
+    # Type specified, None allowed
+    @overload
+    def iter(
+        self,
+        *,
+        type: Type[TJSONValue],
+        allow_none: Literal[True],
+        skip_empty: bool = ...,
+        skip_invalid: bool = ...,
+    ) -> Iterator[Optional[TJSONValue]]:
+        ...  # pragma: no cover
+
+    # Generic definition
+    @overload
+    def iter(
+        self,
+        *,
+        type: Optional[Type[TJSONValue]] = ...,
+        allow_none: bool = ...,
+        skip_empty: bool = ...,
+        skip_invalid: bool = ...,
+    ) -> Iterator[Optional[TJSONValue]]:
+        ...  # pragma: no cover
+
+    def iter(
+        self,
+        type: Optional[Type[Any]] = None,
+        allow_none: bool = False,
+        skip_empty: bool = False,
+        skip_invalid: bool = False,
+    ) -> Iterator[Optional[JSONValue]]:
+        """
+        Iterate over all lines.
+
+        This is the iterator equivalent to repeatedly calling
+        :py:meth:`~Reader.read()`. If no arguments are specified, this
+        is the same as directly iterating over this :py:class:`Reader`
+        instance.
+
+        When `skip_invalid` is set to ``True``, invalid lines will be
+        silently ignored.
+
+        See :py:meth:`~Reader.read()` for a description of the other
+        arguments.
+        """
+        try:
+            while True:
+                try:
+                    yield self.read(
+                        type=type, allow_none=allow_none, skip_empty=skip_empty
+                    )
+                except InvalidLineError:
+                    if not skip_invalid:
+                        raise
+        except EOFError:
+            pass
+
+    def __iter__(self) -> Iterator[Any]:
+        """
+        See :py:meth:`~Reader.iter()`.
+        """
+        return self.iter()
+
+    def _repr_for_wrapped(self) -> str:
+        if self._fp is not None:
+            return repr_for_fp(self._fp)
+        class_name = type(self._file_or_iterable).__name__
+        return f"<{class_name} at 0x{id(self._file_or_iterable):x}>"
+
+
+@attr.s(auto_attribs=True, repr=False)
+class Writer(ReaderWriterBase):
+    """
+    Writer for the jsonlines format.
+
+    Instances can be used as a context manager.
+
+    The `fp` argument must be a file-like object with a ``.write()``
+    method accepting either text (unicode) or bytes.
+
+    The `compact` argument can be used to to produce smaller output.
+
+    The `sort_keys` argument can be used to sort keys in json objects,
+    and will produce deterministic output.
+
+    For more control, provide a a custom encoder callable using the
+    `dumps` argument. The callable must produce (unicode) string output.
+    If specified, the `compact` and `sort` arguments will be ignored.
+
+    When the `flush` argument is set to ``True``, the writer will call
+    ``fp.flush()`` after each written line.
+
+    :param fp: writable file-like object
+    :param compact: whether to use a compact output format
+    :param sort_keys: whether to sort object keys
+    :param dumps: custom encoder callable
+    :param flush: whether to flush the file-like object after writing each line
+    """
+
+    _fp: Union[typing.IO[str], typing.IO[bytes]] = attr.ib(default=None)
+    _fp_is_binary: bool = attr.ib(default=False, init=False)
+    _compact: bool = attr.ib(default=False, kw_only=True)
+    _sort_keys: bool = attr.ib(default=False, kw_only=True)
+    _flush: bool = attr.ib(default=False, kw_only=True)
+    _dumps: DumpsCallable = attr.ib(default=default_dumps, kw_only=True)
+    _dumps_result_conversion: DumpsResultConversion = attr.ib(
+        default=DumpsResultConversion.LeaveAsIs, init=False
+    )
+
+    def __attrs_post_init__(self) -> None:
+        if isinstance(self._fp, io.TextIOBase):
+            self._fp_is_binary = False
+        elif isinstance(self._fp, io.IOBase):
+            self._fp_is_binary = True
+        else:
+            try:
+                self._fp.write("")  # type: ignore[call-overload]
+            except TypeError:
+                self._fp_is_binary = True
+            else:
+                self._fp_is_binary = False
+
+        if self._dumps is default_dumps:
+            self._dumps = json.JSONEncoder(
+                ensure_ascii=False,
+                separators=(",", ":") if self._compact else (", ", ": "),
+                sort_keys=self._sort_keys,
+            ).encode
+
+        # Detect if str-to-bytes conversion (or vice versa) is needed for the
+        # combination of this file-like object and the used dumps() callable.
+        # This avoids checking this for each .write(). Note that this
+        # deliberately does not support ‘dynamic’ return types that depend on
+        # input and dump options, like simplejson on Python 2 in some cases.
+        sample_dumps_result = self._dumps({})
+        if isinstance(sample_dumps_result, str) and self._fp_is_binary:
+            self._dumps_result_conversion = DumpsResultConversion.EncodeToBytes
+        elif isinstance(sample_dumps_result, bytes) and not self._fp_is_binary:
+            self._dumps_result_conversion = DumpsResultConversion.DecodeToString
+
+    def write(self, obj: Any) -> int:
+        """
+        Encode and write a single object.
+
+        :param obj: the object to encode and write
+        :return: number of characters or bytes written
+        """
+        if self._closed:
+            raise RuntimeError("writer is closed")
+
+        line = self._dumps(obj)
+
+        # This handles either str or bytes, but the type checker does not know
+        # that this code always passes the right type of arguments.
+        if self._dumps_result_conversion == DumpsResultConversion.EncodeToBytes:
+            line = line.encode()  # type: ignore[union-attr]
+        elif self._dumps_result_conversion == DumpsResultConversion.DecodeToString:
+            line = line.decode()  # type: ignore[union-attr]
+
+        fp = self._fp
+        fp.write(line)  # type: ignore[arg-type]
+        fp.write(b"\n" if self._fp_is_binary else "\n")  # type: ignore[call-overload]
+
+        if self._flush:
+            fp.flush()
+
+        return len(line) + 1  # including newline
+
+    def write_all(self, iterable: Iterable[Any]) -> int:
+        """
+        Encode and write multiple objects.
+
+        :param iterable: an iterable of objects
+        :return: number of characters or bytes written
+        """
+        return sum(self.write(obj) for obj in iterable)
+
+    def _repr_for_wrapped(self) -> str:
+        return repr_for_fp(self._fp)
+
+
+@overload
+def open(
+    file: Openable,
+    mode: Literal["r"] = ...,
+    *,
+    loads: Optional[LoadsCallable] = ...,
+) -> Reader:
+    ...  # pragma: no cover
+
+
+@overload
+def open(
+    file: Openable,
+    mode: Literal["w", "a", "x"],
+    *,
+    dumps: Optional[DumpsCallable] = ...,
+    compact: Optional[bool] = ...,
+    sort_keys: Optional[bool] = ...,
+    flush: Optional[bool] = ...,
+) -> Writer:
+    ...  # pragma: no cover
+
+
+@overload
+def open(
+    file: Openable,
+    mode: str = ...,
+    *,
+    loads: Optional[LoadsCallable] = ...,
+    dumps: Optional[DumpsCallable] = ...,
+    compact: Optional[bool] = ...,
+    sort_keys: Optional[bool] = ...,
+    flush: Optional[bool] = ...,
+) -> Union[Reader, Writer]:
+    ...  # pragma: no cover
+
+
+def open(
+    file: Openable,
+    mode: str = "r",
+    *,
+    loads: Optional[LoadsCallable] = None,
+    dumps: Optional[DumpsCallable] = None,
+    compact: Optional[bool] = None,
+    sort_keys: Optional[bool] = None,
+    flush: Optional[bool] = None,
+) -> Union[Reader, Writer]:
+    """
+    Open a jsonlines file for reading or writing.
+
+    This is a convenience function to open a file and wrap it in either a
+    :py:class:`Reader` or :py:class:`Writer` instance, depending on the
+    specified `mode`.
+
+    Additional keyword arguments will be passed on to the reader and writer;
+    see their documentation for available options.
+
+    The resulting reader or writer must be closed after use by the
+    caller, which will also close the opened file.  This can be done by
+    calling ``.close()``, but the easiest way to ensure proper resource
+    finalisation is to use a ``with`` block (context manager), e.g.
+
+    ::
+
+        with jsonlines.open('out.jsonl', mode='w') as writer:
+            writer.write(...)
+
+    :param file: name or ‘path-like object’ of the file to open
+    :param mode: whether to open the file for reading (``r``),
+        writing (``w``), appending (``a``), or exclusive creation (``x``).
+    """
+    if mode not in {"r", "w", "a", "x"}:
+        raise ValueError("'mode' must be either 'r', 'w', 'a', or 'x'")
+
+    cls = Reader if mode == "r" else Writer
+    encoding = "utf-8-sig" if mode == "r" else "utf-8"
+    fp = builtins.open(file, mode=mode + "t", encoding=encoding)
+    kwargs = dict(
+        loads=loads,
+        dumps=dumps,
+        compact=compact,
+        sort_keys=sort_keys,
+        flush=flush,
+    )
+    kwargs = {key: value for key, value in kwargs.items() if value is not None}
+    instance: Union[Reader, Writer] = cls(fp, **kwargs)
+    instance._should_close_fp = True
+    return instance
+
+
+def repr_for_fp(fp: typing.IO[Any]) -> str:
+    """
+    Helper to make a useful repr() for a file-like object.
+    """
+    name = getattr(fp, "name", None)
+    if name is not None:
+        return repr(name)
+    else:
+        return repr(fp)

venv/lib/python3.10/site-packages/pydantic-2.7.1.dist-info/licenses/LICENSE

@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2017 to present Pydantic Services Inc. and individual contributors.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

venv/lib/python3.10/site-packages/pytz-2024.1.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

venv/lib/python3.10/site-packages/pytz-2024.1.dist-info/LICENSE.txt

@@ -0,0 +1,19 @@
+Copyright (c) 2003-2019 Stuart Bishop <[email protected]>
+
+Permission is hereby granted, free of charge, to any person obtaining a
+copy of this software and associated documentation files (the "Software"),
+to deal in the Software without restriction, including without limitation
+the rights to use, copy, modify, merge, publish, distribute, sublicense,
+and/or sell copies of the Software, and to permit persons to whom the
+Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.

venv/lib/python3.10/site-packages/pytz-2024.1.dist-info/METADATA

@@ -0,0 +1,649 @@
+Metadata-Version: 2.1
+Name: pytz
+Version: 2024.1
+Summary: World timezone definitions, modern and historical
+Home-page: http://pythonhosted.org/pytz
+Author: Stuart Bishop
+Author-email: [email protected]
+Maintainer: Stuart Bishop
+Maintainer-email: [email protected]
+License: MIT
+Download-URL: https://pypi.org/project/pytz/
+Keywords: timezone,tzinfo,datetime,olson,time
+Platform: Independent
+Classifier: Development Status :: 6 - Mature
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Natural Language :: English
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 2
+Classifier: Programming Language :: Python :: 2.4
+Classifier: Programming Language :: Python :: 2.5
+Classifier: Programming Language :: Python :: 2.6
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.1
+Classifier: Programming Language :: Python :: 3.2
+Classifier: Programming Language :: Python :: 3.3
+Classifier: Programming Language :: Python :: 3.4
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+License-File: LICENSE.txt
+
+pytz - World Timezone Definitions for Python
+============================================
+
+:Author: Stuart Bishop <[email protected]>
+
+Introduction
+~~~~~~~~~~~~
+
+pytz brings the Olson tz database into Python. This library allows
+accurate and cross platform timezone calculations using Python 2.4
+or higher. It also solves the issue of ambiguous times at the end
+of daylight saving time, which you can read more about in the Python
+Library Reference (``datetime.tzinfo``).
+
+Almost all of the Olson timezones are supported.
+
+.. note::
+
+    Projects using Python 3.9 or later should be using the support
+    now included as part of the standard library, and third party
+    packages work with it such as `tzdata <https://pypi.org/project/tzdata/>`_.
+    pytz offers no advantages beyond backwards compatibility with
+    code written for earlier versions of Python.
+
+.. note::
+
+    This library differs from the documented Python API for
+    tzinfo implementations; if you want to create local wallclock
+    times you need to use the ``localize()`` method documented in this
+    document. In addition, if you perform date arithmetic on local
+    times that cross DST boundaries, the result may be in an incorrect
+    timezone (ie. subtract 1 minute from 2002-10-27 1:00 EST and you get
+    2002-10-27 0:59 EST instead of the correct 2002-10-27 1:59 EDT). A
+    ``normalize()`` method is provided to correct this. Unfortunately these
+    issues cannot be resolved without modifying the Python datetime
+    implementation (see PEP-431).
+
+
+Installation
+~~~~~~~~~~~~
+
+This package can either be installed using ``pip`` or from a tarball using the
+standard Python distutils.
+
+If you are installing using ``pip``, you don't need to download anything as the
+latest version will be downloaded for you from PyPI::
+
+    pip install pytz
+
+If you are installing from a tarball, run the following command as an
+administrative user::
+
+    python setup.py install
+
+
+pytz for Enterprise
+~~~~~~~~~~~~~~~~~~~
+
+Available as part of the Tidelift Subscription.
+
+The maintainers of pytz and thousands of other packages are working with Tidelift to deliver commercial support and maintenance for the open source dependencies you use to build your applications. Save time, reduce risk, and improve code health, while paying the maintainers of the exact dependencies you use. `Learn more. <https://tidelift.com/subscription/pkg/pypi-pytz?utm_source=pypi-pytz&utm_medium=referral&utm_campaign=enterprise&utm_term=repo>`_.
+
+
+Example & Usage
+~~~~~~~~~~~~~~~
+
+Localized times and date arithmetic
+-----------------------------------
+
+>>> from datetime import datetime, timedelta
+>>> from pytz import timezone
+>>> import pytz
+>>> utc = pytz.utc
+>>> utc.zone
+'UTC'
+>>> eastern = timezone('US/Eastern')
+>>> eastern.zone
+'US/Eastern'
+>>> amsterdam = timezone('Europe/Amsterdam')
+>>> fmt = '%Y-%m-%d %H:%M:%S %Z%z'
+
+This library only supports two ways of building a localized time. The
+first is to use the ``localize()`` method provided by the pytz library.
+This is used to localize a naive datetime (datetime with no timezone
+information):
+
+>>> loc_dt = eastern.localize(datetime(2002, 10, 27, 6, 0, 0))
+>>> print(loc_dt.strftime(fmt))
+2002-10-27 06:00:00 EST-0500
+
+The second way of building a localized time is by converting an existing
+localized time using the standard ``astimezone()`` method:
+
+>>> ams_dt = loc_dt.astimezone(amsterdam)
+>>> ams_dt.strftime(fmt)
+'2002-10-27 12:00:00 CET+0100'
+
+Unfortunately using the tzinfo argument of the standard datetime
+constructors ''does not work'' with pytz for many timezones.
+
+>>> datetime(2002, 10, 27, 12, 0, 0, tzinfo=amsterdam).strftime(fmt)  # /!\ Does not work this way!
+'2002-10-27 12:00:00 LMT+0018'
+
+It is safe for timezones without daylight saving transitions though, such
+as UTC:
+
+>>> datetime(2002, 10, 27, 12, 0, 0, tzinfo=pytz.utc).strftime(fmt)  # /!\ Not recommended except for UTC
+'2002-10-27 12:00:00 UTC+0000'
+
+The preferred way of dealing with times is to always work in UTC,
+converting to localtime only when generating output to be read
+by humans.
+
+>>> utc_dt = datetime(2002, 10, 27, 6, 0, 0, tzinfo=utc)
+>>> loc_dt = utc_dt.astimezone(eastern)
+>>> loc_dt.strftime(fmt)
+'2002-10-27 01:00:00 EST-0500'
+
+This library also allows you to do date arithmetic using local
+times, although it is more complicated than working in UTC as you
+need to use the ``normalize()`` method to handle daylight saving time
+and other timezone transitions. In this example, ``loc_dt`` is set
+to the instant when daylight saving time ends in the US/Eastern
+timezone.
+
+>>> before = loc_dt - timedelta(minutes=10)
+>>> before.strftime(fmt)
+'2002-10-27 00:50:00 EST-0500'
+>>> eastern.normalize(before).strftime(fmt)
+'2002-10-27 01:50:00 EDT-0400'
+>>> after = eastern.normalize(before + timedelta(minutes=20))
+>>> after.strftime(fmt)
+'2002-10-27 01:10:00 EST-0500'
+
+Creating local times is also tricky, and the reason why working with
+local times is not recommended. Unfortunately, you cannot just pass
+a ``tzinfo`` argument when constructing a datetime (see the next
+section for more details)
+
+>>> dt = datetime(2002, 10, 27, 1, 30, 0)
+>>> dt1 = eastern.localize(dt, is_dst=True)
+>>> dt1.strftime(fmt)
+'2002-10-27 01:30:00 EDT-0400'
+>>> dt2 = eastern.localize(dt, is_dst=False)
+>>> dt2.strftime(fmt)
+'2002-10-27 01:30:00 EST-0500'
+
+Converting between timezones is more easily done, using the
+standard astimezone method.
+
+>>> utc_dt = datetime.fromtimestamp(1143408899, tz=utc)
+>>> utc_dt.strftime(fmt)
+'2006-03-26 21:34:59 UTC+0000'
+>>> au_tz = timezone('Australia/Sydney')
+>>> au_dt = utc_dt.astimezone(au_tz)
+>>> au_dt.strftime(fmt)
+'2006-03-27 08:34:59 AEDT+1100'
+>>> utc_dt2 = au_dt.astimezone(utc)
+>>> utc_dt2.strftime(fmt)
+'2006-03-26 21:34:59 UTC+0000'
+>>> utc_dt == utc_dt2
+True
+
+You can take shortcuts when dealing with the UTC side of timezone
+conversions. ``normalize()`` and ``localize()`` are not really
+necessary when there are no daylight saving time transitions to
+deal with.
+
+>>> utc_dt = datetime.fromtimestamp(1143408899, tz=utc)
+>>> utc_dt.strftime(fmt)
+'2006-03-26 21:34:59 UTC+0000'
+>>> au_tz = timezone('Australia/Sydney')
+>>> au_dt = au_tz.normalize(utc_dt.astimezone(au_tz))
+>>> au_dt.strftime(fmt)
+'2006-03-27 08:34:59 AEDT+1100'
+>>> utc_dt2 = au_dt.astimezone(utc)
+>>> utc_dt2.strftime(fmt)
+'2006-03-26 21:34:59 UTC+0000'
+
+
+``tzinfo`` API
+--------------
+
+The ``tzinfo`` instances returned by the ``timezone()`` function have
+been extended to cope with ambiguous times by adding an ``is_dst``
+parameter to the ``utcoffset()``, ``dst()`` && ``tzname()`` methods.
+
+>>> tz = timezone('America/St_Johns')
+
+>>> normal = datetime(2009, 9, 1)
+>>> ambiguous = datetime(2009, 10, 31, 23, 30)
+
+The ``is_dst`` parameter is ignored for most timestamps. It is only used
+during DST transition ambiguous periods to resolve that ambiguity.
+
+>>> print(tz.utcoffset(normal, is_dst=True))
+-1 day, 21:30:00
+>>> print(tz.dst(normal, is_dst=True))
+1:00:00
+>>> tz.tzname(normal, is_dst=True)
+'NDT'
+
+>>> print(tz.utcoffset(ambiguous, is_dst=True))
+-1 day, 21:30:00
+>>> print(tz.dst(ambiguous, is_dst=True))
+1:00:00
+>>> tz.tzname(ambiguous, is_dst=True)
+'NDT'
+
+>>> print(tz.utcoffset(normal, is_dst=False))
+-1 day, 21:30:00
+>>> tz.dst(normal, is_dst=False).seconds
+3600
+>>> tz.tzname(normal, is_dst=False)
+'NDT'
+
+>>> print(tz.utcoffset(ambiguous, is_dst=False))
+-1 day, 20:30:00
+>>> tz.dst(ambiguous, is_dst=False)
+datetime.timedelta(0)
+>>> tz.tzname(ambiguous, is_dst=False)
+'NST'
+
+If ``is_dst`` is not specified, ambiguous timestamps will raise
+an ``pytz.exceptions.AmbiguousTimeError`` exception.
+
+>>> print(tz.utcoffset(normal))
+-1 day, 21:30:00
+>>> print(tz.dst(normal))
+1:00:00
+>>> tz.tzname(normal)
+'NDT'
+
+>>> import pytz.exceptions
+>>> try:
+...     tz.utcoffset(ambiguous)
+... except pytz.exceptions.AmbiguousTimeError:
+...     print('pytz.exceptions.AmbiguousTimeError: %s' % ambiguous)
+pytz.exceptions.AmbiguousTimeError: 2009-10-31 23:30:00
+>>> try:
+...     tz.dst(ambiguous)
+... except pytz.exceptions.AmbiguousTimeError:
+...     print('pytz.exceptions.AmbiguousTimeError: %s' % ambiguous)
+pytz.exceptions.AmbiguousTimeError: 2009-10-31 23:30:00
+>>> try:
+...     tz.tzname(ambiguous)
+... except pytz.exceptions.AmbiguousTimeError:
+...     print('pytz.exceptions.AmbiguousTimeError: %s' % ambiguous)
+pytz.exceptions.AmbiguousTimeError: 2009-10-31 23:30:00
+
+
+Problems with Localtime
+~~~~~~~~~~~~~~~~~~~~~~~
+
+The major problem we have to deal with is that certain datetimes
+may occur twice in a year. For example, in the US/Eastern timezone
+on the last Sunday morning in October, the following sequence
+happens:
+
+    - 01:00 EDT occurs
+    - 1 hour later, instead of 2:00am the clock is turned back 1 hour
+      and 01:00 happens again (this time 01:00 EST)
+
+In fact, every instant between 01:00 and 02:00 occurs twice. This means
+that if you try and create a time in the 'US/Eastern' timezone
+the standard datetime syntax, there is no way to specify if you meant
+before of after the end-of-daylight-saving-time transition. Using the
+pytz custom syntax, the best you can do is make an educated guess:
+
+>>> loc_dt = eastern.localize(datetime(2002, 10, 27, 1, 30, 00))
+>>> loc_dt.strftime(fmt)
+'2002-10-27 01:30:00 EST-0500'
+
+As you can see, the system has chosen one for you and there is a 50%
+chance of it being out by one hour. For some applications, this does
+not matter. However, if you are trying to schedule meetings with people
+in different timezones or analyze log files it is not acceptable.
+
+The best and simplest solution is to stick with using UTC.  The pytz
+package encourages using UTC for internal timezone representation by
+including a special UTC implementation based on the standard Python
+reference implementation in the Python documentation.
+
+The UTC timezone unpickles to be the same instance, and pickles to a
+smaller size than other pytz tzinfo instances.  The UTC implementation
+can be obtained as pytz.utc, pytz.UTC, or pytz.timezone('UTC').
+
+>>> import pickle, pytz
+>>> dt = datetime(2005, 3, 1, 14, 13, 21, tzinfo=utc)
+>>> naive = dt.replace(tzinfo=None)
+>>> p = pickle.dumps(dt, 1)
+>>> naive_p = pickle.dumps(naive, 1)
+>>> len(p) - len(naive_p)
+17
+>>> new = pickle.loads(p)
+>>> new == dt
+True
+>>> new is dt
+False
+>>> new.tzinfo is dt.tzinfo
+True
+>>> pytz.utc is pytz.UTC is pytz.timezone('UTC')
+True
+
+Note that some other timezones are commonly thought of as the same (GMT,
+Greenwich, Universal, etc.). The definition of UTC is distinct from these
+other timezones, and they are not equivalent. For this reason, they will
+not compare the same in Python.
+
+>>> utc == pytz.timezone('GMT')
+False
+
+See the section `What is UTC`_, below.
+
+If you insist on working with local times, this library provides a
+facility for constructing them unambiguously:
+
+>>> loc_dt = datetime(2002, 10, 27, 1, 30, 00)
+>>> est_dt = eastern.localize(loc_dt, is_dst=True)
+>>> edt_dt = eastern.localize(loc_dt, is_dst=False)
+>>> print(est_dt.strftime(fmt) + ' / ' + edt_dt.strftime(fmt))
+2002-10-27 01:30:00 EDT-0400 / 2002-10-27 01:30:00 EST-0500
+
+If you pass None as the is_dst flag to localize(), pytz will refuse to
+guess and raise exceptions if you try to build ambiguous or non-existent
+times.
+
+For example, 1:30am on 27th Oct 2002 happened twice in the US/Eastern
+timezone when the clocks where put back at the end of Daylight Saving
+Time:
+
+>>> dt = datetime(2002, 10, 27, 1, 30, 00)
+>>> try:
+...     eastern.localize(dt, is_dst=None)
+... except pytz.exceptions.AmbiguousTimeError:
+...     print('pytz.exceptions.AmbiguousTimeError: %s' % dt)
+pytz.exceptions.AmbiguousTimeError: 2002-10-27 01:30:00
+
+Similarly, 2:30am on 7th April 2002 never happened at all in the
+US/Eastern timezone, as the clocks where put forward at 2:00am skipping
+the entire hour:
+
+>>> dt = datetime(2002, 4, 7, 2, 30, 00)
+>>> try:
+...     eastern.localize(dt, is_dst=None)
+... except pytz.exceptions.NonExistentTimeError:
+...     print('pytz.exceptions.NonExistentTimeError: %s' % dt)
+pytz.exceptions.NonExistentTimeError: 2002-04-07 02:30:00
+
+Both of these exceptions share a common base class to make error handling
+easier:
+
+>>> isinstance(pytz.AmbiguousTimeError(), pytz.InvalidTimeError)
+True
+>>> isinstance(pytz.NonExistentTimeError(), pytz.InvalidTimeError)
+True
+
+
+A special case is where countries change their timezone definitions
+with no daylight savings time switch. For example, in 1915 Warsaw
+switched from Warsaw time to Central European time with no daylight savings
+transition. So at the stroke of midnight on August 5th 1915 the clocks
+were wound back 24 minutes creating an ambiguous time period that cannot
+be specified without referring to the timezone abbreviation or the
+actual UTC offset. In this case midnight happened twice, neither time
+during a daylight saving time period. pytz handles this transition by
+treating the ambiguous period before the switch as daylight savings
+time, and the ambiguous period after as standard time.
+
+
+>>> warsaw = pytz.timezone('Europe/Warsaw')
+>>> amb_dt1 = warsaw.localize(datetime(1915, 8, 4, 23, 59, 59), is_dst=True)
+>>> amb_dt1.strftime(fmt)
+'1915-08-04 23:59:59 WMT+0124'
+>>> amb_dt2 = warsaw.localize(datetime(1915, 8, 4, 23, 59, 59), is_dst=False)
+>>> amb_dt2.strftime(fmt)
+'1915-08-04 23:59:59 CET+0100'
+>>> switch_dt = warsaw.localize(datetime(1915, 8, 5, 00, 00, 00), is_dst=False)
+>>> switch_dt.strftime(fmt)
+'1915-08-05 00:00:00 CET+0100'
+>>> str(switch_dt - amb_dt1)
+'0:24:01'
+>>> str(switch_dt - amb_dt2)
+'0:00:01'
+
+The best way of creating a time during an ambiguous time period is
+by converting from another timezone such as UTC:
+
+>>> utc_dt = datetime(1915, 8, 4, 22, 36, tzinfo=pytz.utc)
+>>> utc_dt.astimezone(warsaw).strftime(fmt)
+'1915-08-04 23:36:00 CET+0100'
+
+The standard Python way of handling all these ambiguities is not to
+handle them, such as demonstrated in this example using the US/Eastern
+timezone definition from the Python documentation (Note that this
+implementation only works for dates between 1987 and 2006 - it is
+included for tests only!):
+
+>>> from pytz.reference import Eastern # pytz.reference only for tests
+>>> dt = datetime(2002, 10, 27, 0, 30, tzinfo=Eastern)
+>>> str(dt)
+'2002-10-27 00:30:00-04:00'
+>>> str(dt + timedelta(hours=1))
+'2002-10-27 01:30:00-05:00'
+>>> str(dt + timedelta(hours=2))
+'2002-10-27 02:30:00-05:00'
+>>> str(dt + timedelta(hours=3))
+'2002-10-27 03:30:00-05:00'
+
+Notice the first two results? At first glance you might think they are
+correct, but taking the UTC offset into account you find that they are
+actually two hours appart instead of the 1 hour we asked for.
+
+>>> from pytz.reference import UTC # pytz.reference only for tests
+>>> str(dt.astimezone(UTC))
+'2002-10-27 04:30:00+00:00'
+>>> str((dt + timedelta(hours=1)).astimezone(UTC))
+'2002-10-27 06:30:00+00:00'
+
+
+Country Information
+~~~~~~~~~~~~~~~~~~~
+
+A mechanism is provided to access the timezones commonly in use
+for a particular country, looked up using the ISO 3166 country code.
+It returns a list of strings that can be used to retrieve the relevant
+tzinfo instance using ``pytz.timezone()``:
+
+>>> print(' '.join(pytz.country_timezones['nz']))
+Pacific/Auckland Pacific/Chatham
+
+The Olson database comes with a ISO 3166 country code to English country
+name mapping that pytz exposes as a dictionary:
+
+>>> print(pytz.country_names['nz'])
+New Zealand
+
+
+What is UTC
+~~~~~~~~~~~
+
+'UTC' is `Coordinated Universal Time`_. It is a successor to, but distinct
+from, Greenwich Mean Time (GMT) and the various definitions of Universal
+Time. UTC is now the worldwide standard for regulating clocks and time
+measurement.
+
+All other timezones are defined relative to UTC, and include offsets like
+UTC+0800 - hours to add or subtract from UTC to derive the local time. No
+daylight saving time occurs in UTC, making it a useful timezone to perform
+date arithmetic without worrying about the confusion and ambiguities caused
+by daylight saving time transitions, your country changing its timezone, or
+mobile computers that roam through multiple timezones.
+
+..  _Coordinated Universal Time: https://en.wikipedia.org/wiki/Coordinated_Universal_Time
+
+
+Helpers
+~~~~~~~
+
+There are two lists of timezones provided.
+
+``all_timezones`` is the exhaustive list of the timezone names that can
+be used.
+
+>>> from pytz import all_timezones
+>>> len(all_timezones) >= 500
+True
+>>> 'Etc/Greenwich' in all_timezones
+True
+
+``common_timezones`` is a list of useful, current timezones. It doesn't
+contain deprecated zones or historical zones, except for a few I've
+deemed in common usage, such as US/Eastern (open a bug report if you
+think other timezones are deserving of being included here). It is also
+a sequence of strings.
+
+>>> from pytz import common_timezones
+>>> len(common_timezones) < len(all_timezones)
+True
+>>> 'Etc/Greenwich' in common_timezones
+False
+>>> 'Australia/Melbourne' in common_timezones
+True
+>>> 'US/Eastern' in common_timezones
+True
+>>> 'Canada/Eastern' in common_timezones
+True
+>>> 'Australia/Yancowinna' in all_timezones
+True
+>>> 'Australia/Yancowinna' in common_timezones
+False
+
+Both ``common_timezones`` and ``all_timezones`` are alphabetically
+sorted:
+
+>>> common_timezones_dupe = common_timezones[:]
+>>> common_timezones_dupe.sort()
+>>> common_timezones == common_timezones_dupe
+True
+>>> all_timezones_dupe = all_timezones[:]
+>>> all_timezones_dupe.sort()
+>>> all_timezones == all_timezones_dupe
+True
+
+``all_timezones`` and ``common_timezones`` are also available as sets.
+
+>>> from pytz import all_timezones_set, common_timezones_set
+>>> 'US/Eastern' in all_timezones_set
+True
+>>> 'US/Eastern' in common_timezones_set
+True
+>>> 'Australia/Victoria' in common_timezones_set
+False
+
+You can also retrieve lists of timezones used by particular countries
+using the ``country_timezones()`` function. It requires an ISO-3166
+two letter country code.
+
+>>> from pytz import country_timezones
+>>> print(' '.join(country_timezones('ch')))
+Europe/Zurich
+>>> print(' '.join(country_timezones('CH')))
+Europe/Zurich
+
+
+Internationalization - i18n/l10n
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Pytz is an interface to the IANA database, which uses ASCII names. The `Unicode  Consortium's Unicode Locales (CLDR) <http://cldr.unicode.org>`_
+project provides translations. Python packages such as
+`Babel <https://babel.pocoo.org/en/latest/api/dates.html#timezone-functionality>`_
+and Thomas Khyn's `l18n <https://pypi.org/project/l18n/>`_ package can be used
+to access these translations from Python.
+
+
+License
+~~~~~~~
+
+MIT license.
+
+This code is also available as part of Zope 3 under the Zope Public
+License,  Version 2.1 (ZPL).
+
+I'm happy to relicense this code if necessary for inclusion in other
+open source projects.
+
+
+Latest Versions
+~~~~~~~~~~~~~~~
+
+This package will be updated after releases of the Olson timezone
+database.  The latest version can be downloaded from the `Python Package
+Index <https://pypi.org/project/pytz/>`_.  The code that is used
+to generate this distribution is hosted on Github and available
+using git::
+
+    git clone https://github.com/stub42/pytz.git
+
+Announcements of new releases are made on
+`Launchpad <https://launchpad.net/pytz>`_, and the
+`Atom feed <http://feeds.launchpad.net/pytz/announcements.atom>`_
+hosted there.
+
+
+Bugs, Feature Requests & Patches
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Bugs should be reported on `Github <https://github.com/stub42/pytz/issues>`_.
+Feature requests are unlikely to be considered, and efforts instead directed
+to timezone support now built into Python or packages that work with it.
+
+
+Security Issues
+~~~~~~~~~~~~~~~
+
+Reports about security issues can be made via `Tidelift <https://tidelift.com/security>`_.
+
+
+Issues & Limitations
+~~~~~~~~~~~~~~~~~~~~
+
+- This project is in maintenance mode. Projects using Python 3.9 or later
+  are best served by using the timezone functionaly now included in core
+  Python and packages that work with it such as `tzdata <https://pypi.org/project/tzdata/>`_.
+
+- Offsets from UTC are rounded to the nearest whole minute, so timezones
+  such as Europe/Amsterdam pre 1937 will be up to 30 seconds out. This
+  was a limitation of the Python datetime library.
+
+- If you think a timezone definition is incorrect, I probably can't fix
+  it. pytz is a direct translation of the Olson timezone database, and
+  changes to the timezone definitions need to be made to this source.
+  If you find errors they should be reported to the time zone mailing
+  list, linked from http://www.iana.org/time-zones.
+
+
+Further Reading
+~~~~~~~~~~~~~~~
+
+More info than you want to know about timezones:
+https://data.iana.org/time-zones/tz-link.html
+
+
+Contact
+~~~~~~~
+
+Stuart Bishop <[email protected]>
+
+

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