"""simple docstring"""




from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    AutoTokenizer,
				    TFAutoModelForCausalLM,
				    TFAutoModelForSeqaSeqLM,
				    TFAutoModelForSpeechSeqaSeq,
				    TFAutoModelForVisionaSeq,
				    TFBartForConditionalGeneration,
				    TFLogitsProcessorList,
				    TFMinLengthLogitsProcessor,
				    tf_top_k_top_p_filtering,
				)

if is_tensorflow_text_available():
				import tensorflow_text as text
@require_tf
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				int:
													A__	     =							tf.convert_to_tensor(
													    [
													        [
													            8.2_2_2_0_9_9_1,  # 3rd highest value; idx. 0
													            -0.5_6_2_0_0_4_4,
													            5.2_3_2_2_9_7_5_2,
													            4.0_3_8_6_3_9_3,
													            -6.8_7_9_8_3_7_8,
													            -0.5_4_7_8_5_8_0_2,
													            -3.2_0_1_2_1_5_3,
													            2.9_2_7_7_7_1_7_6,
													            1.8_8_1_7_1_9_5_3,
													            7.3_5_3_4_1_2_7_6,  # 5th highest value; idx. 9
													            8.4_3_2_0_7_8_3_3,  # 2nd highest value; idx. 10
													            -9.8_5_7_1_1_8_3_6,
													            -5.9_6_2_0_9_2_3_6,
													            -1.1_3_0_3_9_1_6_1,
													            -7.1_1_1_5_2_9_4,
													            -0.8_3_6_9_6_3_3,
													            -5.3_1_8_6_4_0_8,
													            7.0_6_4_2_7_4_0_7,
													            0.8_1_3_6_9_3_4_4,
													            -0.8_2_0_2_3_8_1_7,
													            -5.9_1_7_9_7_9_6,
													            0.5_8_8_1_3_4_4_3,
													            -6.9_9_7_7_8_4_3_8,
													            4.7_1_5_5_1_1_8_9,
													            -0.1_8_7_7_1_6_3_7,
													            7.4_4_0_2_0_7_5_9,  # 4th highest value; idx. 25
													            9.3_8_4_5_0_9_8_7,  # 1st highest value; idx. 26
													            2.1_2_6_6_2_9_4_1,
													            -9.3_2_5_6_2_0_3_8,
													            2.3_5_6_5_2_5_2_2,
													        ],  # cummulative prob of 5 highest values <= 0.6
													        [
													            0.5_8_4_2_5_5_1_8,
													            4.5_3_1_3_9_2_3_8,
													            -5.5_7_5_1_0_4_6_4,
													            -6.2_8_0_3_0_6_9_9,
													            -7.1_9_5_2_9_5_0_3,
													            -4.0_2_1_2_2_5_5_1,
													            1.3_9_3_3_7_0_3_7,
													            -6.0_6_7_0_7_0_5_7,
													            1.5_9_4_8_0_5_1_7,
													            -9.6_4_3_1_1_9,
													            0.0_3_9_0_7_7_9_9,
													            0.6_7_2_3_1_7_6_2,
													            -8.8_8_2_0_6_7_2_6,
													            6.2_7_1_1_5_9_2_2,  # 4th highest value; idx. 13
													            2.2_8_5_2_0_7_2_3,
													            4.8_2_7_6_7_5_0_6,
													            4.3_0_4_2_1_3_6_8,
													            8.8_2_7_5_3_1_3,  # 2nd highest value; idx. 17
													            5.4_4_0_2_9_9_5_8,  # 5th highest value; idx. 18
													            -4.4_7_3_5_7_9_4,
													            7.3_8_5_7_9_5_3_6,  # 3rd highest value; idx. 20
													            -2.9_1_0_5_1_6_6_3,
													            2.6_1_9_4_6_0_7_7,
													            -2.5_6_7_4_7_6_2,
													            -9.4_8_9_5_9_3_0_2,
													            -4.0_2_9_2_2_6_4_5,
													            -1.3_5_4_1_6_9_1_8,
													            9.6_7_7_0_2_3_2_3,  # 1st highest value; idx. 27
													            -5.8_9_4_7_8_5_5_3,
													            1.8_5_3_7_0_4_6_7,
													        ],  # cummulative prob of 5 highest values <= 0.6
													    ]		,dtype=tf.floataa		,)

													A__	     =							tf.convert_to_tensor(
													    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]		,dtype=tf.intaa		,)  # expected non filtered idx as noted above

													A__	     =							tf.convert_to_tensor(
													    [8.2_2_2_0_9_9, 7.3_5_3_4_1_2_6, 8.4_3_2_0_7_8, 7.4_4_0_2_0_7_5, 9.3_8_4_5_1, 6.2_7_1_1_5_9, 8.8_2_7_5_3_1, 5.4_4_0_2_9_9_5, 7.3_8_5_7_9_5_6, 9.6_7_7_0_2_3]		,dtype=tf.floataa		,)  # expected non filtered values as noted above

													A__	     =							tf_top_k_top_p_filtering(__UpperCAmelCase		,top_k=10		,top_p=0.6		,min_tokens_to_keep=4     )

													A__	     =							output[output != -float('inf'     )]
													A__	     =							tf.cast(
													    tf.where(tf.not_equal(__UpperCAmelCase		,tf.constant(-float('inf'     )		,dtype=tf.floataa     )     )     )		,dtype=tf.intaa		,)

													tf.debugging.assert_near(__UpperCAmelCase		,__UpperCAmelCase		,rtol=1e-12     )
													tf.debugging.assert_equal(__UpperCAmelCase		,__UpperCAmelCase     )






@require_tf
class     UpperCamelCase__(			unittest.TestCase		,						__A ):
						# setting framework_dependent_parameters needs to be gated, just like its contents' imports
						if is_tf_available():
												lowerCAmelCase__  :						Union[str, Any]													=		{
												    'AutoModelForCausalLM': TFAutoModelForCausalLM,
												    'AutoModelForSpeechSeq2Seq': TFAutoModelForSpeechSeqaSeq,
												    'AutoModelForSeq2SeqLM': TFAutoModelForSeqaSeqLM,
												    'AutoModelForVision2Seq': TFAutoModelForVisionaSeq,
												    'LogitsProcessorList': TFLogitsProcessorList,
												    'MinLengthLogitsProcessor': TFMinLengthLogitsProcessor,
												    'create_tensor_fn': tf.convert_to_tensor,
												    'floats_tensor': floats_tensor,
												    'return_tensors': 'tf',
												}





						@slow
						def 				snake_case__	(       self     )					->				List[str]:
													# TF-only test: tf.saved_model export
													A__	     =							TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'     )
													A__	     =							2
													A__	     =							2
													class     UpperCamelCase__(			tf.Module ):





																			def __init__(       self		,__UpperCAmelCase     )					->				List[Any]:
																										super(__UpperCAmelCase		,self     ).__init__()
																										A__	     =							model





																			@tf.function(
																			    input_signature=(
																			        tf.TensorSpec((None, input_length)		,tf.intaa		,name='input_ids'     ),
																			        tf.TensorSpec((None, input_length)		,tf.intaa		,name='attention_mask'     ),
																			    )		,jit_compile=__UpperCAmelCase		,)
																			def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Optional[Any]:
																										A__	     =							self.model.generate(
																										    input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase		,max_new_tokens=__UpperCAmelCase		,return_dict_in_generate=__UpperCAmelCase		,)
																										return {"sequences": outputs["sequences"]}






													A__	     =							[[2, 0], [1_02, 1_03]]
													A__	     =							[[1, 0], [1, 1]]
													A__	     =							DummyModel(model=__UpperCAmelCase     )
													with tempfile.TemporaryDirectory() as tmp_dir:
																				tf.saved_model.save(__UpperCAmelCase		,__UpperCAmelCase		,signatures={'serving_default': dummy_model.serving}     )
																				A__	     =							tf.saved_model.load(__UpperCAmelCase     ).signatures['serving_default']
																				for batch_size in range(1		,len(__UpperCAmelCase     ) + 1     ):
																											A__	     =							{
																											    'input_ids': tf.constant(dummy_input_ids[:batch_size]     ),
																											    'attention_mask': tf.constant(dummy_attention_masks[:batch_size]     ),
																											}
																											A__	     =							serving_func(**__UpperCAmelCase     )['sequences']
																											A__	     =							test_model.generate(**__UpperCAmelCase		,max_new_tokens=__UpperCAmelCase     )
																											tf.debugging.assert_equal(__UpperCAmelCase		,__UpperCAmelCase     )





						@slow
						def 				snake_case__	(       self     )					->				Dict:
													# TF-only test: tf.saved_model export
													A__	     =							TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'     )
													A__	     =							1
													A__	     =							2
													class     UpperCamelCase__(			tf.Module ):





																			def __init__(       self		,__UpperCAmelCase     )					->				List[Any]:
																										super(__UpperCAmelCase		,self     ).__init__()
																										A__	     =							model





																			@tf.function(
																			    input_signature=(
																			        tf.TensorSpec((batch_size, None)		,tf.intaa		,name='input_ids'     ),
																			        tf.TensorSpec((batch_size, None)		,tf.intaa		,name='attention_mask'     ),
																			    )		,jit_compile=__UpperCAmelCase		,)
																			def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Optional[Any]:
																										A__	     =							self.model.generate(
																										    input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase		,max_new_tokens=__UpperCAmelCase		,return_dict_in_generate=__UpperCAmelCase		,)
																										return {"sequences": outputs["sequences"]}






													A__	     =							[[2], [1_02, 1_03]]
													A__	     =							[[1], [1, 1]]
													A__	     =							DummyModel(model=__UpperCAmelCase     )
													with tempfile.TemporaryDirectory() as tmp_dir:
																				tf.saved_model.save(__UpperCAmelCase		,__UpperCAmelCase		,signatures={'serving_default': dummy_model.serving}     )
																				A__	     =							tf.saved_model.load(__UpperCAmelCase     ).signatures['serving_default']
																				for input_row in range(len(__UpperCAmelCase     )     ):
																											A__	     =							{
																											    'input_ids': tf.constant([dummy_input_ids[input_row]]     ),
																											    'attention_mask': tf.constant([dummy_attention_masks[input_row]]     ),
																											}
																											A__	     =							serving_func(**__UpperCAmelCase     )['sequences']
																											A__	     =							test_model.generate(**__UpperCAmelCase		,max_new_tokens=__UpperCAmelCase     )
																											tf.debugging.assert_equal(__UpperCAmelCase		,__UpperCAmelCase     )





						@slow
						@require_tensorflow_text
						def 				snake_case__	(       self     )					->				Optional[int]:
													# TF-only test: tf.saved_model export
													with tempfile.TemporaryDirectory() as tmp_dir:
																				# file needed to load the TF tokenizer
																				hf_hub_download(repo_id='google/flan-t5-small'		,filename='spiece.model'		,local_dir=__UpperCAmelCase     )
																				class     UpperCamelCase__(			tf.keras.layers.Layer ):





																										def __init__(       self     )					->				List[str]:
																																	super().__init__()
																																	A__	     =							text.SentencepieceTokenizer(
																																	    model=tf.io.gfile.GFile(os.path.join(__UpperCAmelCase		,'spiece.model'     )		,'rb'     ).read()     )
																																	A__	     =							TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5'     )





																										def 				snake_case__	(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[int]:
																																	A__	     =							self.tokenizer.tokenize(__UpperCAmelCase     )
																																	A__     ,  A__	     =							text.pad_model_inputs(
																																	    __UpperCAmelCase		,max_seq_length=64		,pad_value=self.model.config.pad_token_id     )
																																	A__	     =							self.model.generate(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase     )
																																	return self.tokenizer.detokenize(__UpperCAmelCase     )






																				A__	     =							CompleteSentenceTransformer()
																				A__	     =							tf.keras.layers.Input(shape=(1,)		,dtype=tf.string		,name='inputs'     )
																				A__	     =							complete_model(__UpperCAmelCase     )
																				A__	     =							tf.keras.Model(__UpperCAmelCase		,__UpperCAmelCase     )
																				keras_model.save(__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Any:
													# Has PT equivalent: this test relies on random sampling
													A__	     =							{
													    'do_sample': True,
													    'num_beams': 1,
													    'top_p': 0.7,
													    'top_k': 10,
													    'temperature': 0.7,
													}
													A__	     =							14

													A__	     =							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'     )
													A__	     =							'Hello, my dog is cute and'
													A__	     =							tokenizer(__UpperCAmelCase		,return_tensors='tf'     )
													A__	     =							TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'     )

													A__	     =							6_38
													# forces the generation to happen on CPU, to avoid GPU-related quirks
													with tf.device(':/CPU:0'     ):
																				tf.random.set_seed(0     )
																				A__	     =							model.generate(**__UpperCAmelCase		,eos_token_id=__UpperCAmelCase		,**__UpperCAmelCase     )
													self.assertTrue(expectation == len(generated_tokens[0]     )     )

													A__	     =							[6_38, 1_98]
													with tf.device(':/CPU:0'     ):
																				tf.random.set_seed(0     )
																				A__	     =							model.generate(**__UpperCAmelCase		,eos_token_id=__UpperCAmelCase		,**__UpperCAmelCase     )
													self.assertTrue(expectation == len(generated_tokens[0]     )     )





						def 				snake_case__	(       self     )					->				Optional[int]:
													# Has PT equivalent: ample use of framework-specific code
													A__	     =							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart'     )
													A__	     =							'Hugging Face is a technology company based in New York and Paris.'
													A__	     =							bart_tokenizer(__UpperCAmelCase		,return_tensors='tf'     ).input_ids
													A__	     =							TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart'     )
													A__	     =							bart_model.generate(__UpperCAmelCase     ).numpy()
													class     UpperCamelCase__(			__A ):





																			def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=None		,**__UpperCAmelCase     )					->				List[Any]:
																										return super().call(__UpperCAmelCase		,**__UpperCAmelCase     )






													A__	     =							FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart'     )
													A__	     =							bart_model.generate(__UpperCAmelCase		,foo='bar'     ).numpy()
													self.assertTrue(np.array_equal(__UpperCAmelCase		,__UpperCAmelCase     )     )
													class     UpperCamelCase__(			bart_model.model.encoder.__class__ ):





																			def 				snake_case__	(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Union[str, Any]:
																										return super().call(__UpperCAmelCase		,**__UpperCAmelCase     )






													A__	     =							FakeEncoder(bart_model.config		,bart_model.model.shared     )
													A__	     =							fake_encoder

													# Normal generation still works (the output will be different because the encoder weights are different)
													A__	     =							bart_model.generate(__UpperCAmelCase     ).numpy()
													with self.assertRaises(__UpperCAmelCase     ):
																				# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
																				bart_model.generate(__UpperCAmelCase		,foo='bar'     )






"""simple docstring"""




import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

__lowerCamelCase											=  yaml.safe_load(
    "\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubsections:\n  - name: \"Dataset Card for X\" # First-level markdown heading\n    allow_empty: false\n    allow_empty_text: true\n    subsections:\n      - name: \"Table of Contents\"\n        allow_empty: false\n        allow_empty_text: false\n        subsections: null\n      - name: \"Dataset Description\"\n        allow_empty: false\n        allow_empty_text: false\n        subsections:\n          - name: \"Dataset Summary\"\n            allow_empty: false\n            allow_empty_text: false\n            subsections: null\n          - name: \"Supported Tasks and Leaderboards\"\n            allow_empty: true\n            allow_empty_text: true\n            subsections: null\n          - name: Languages\n            allow_empty: false\n            allow_empty_text: true\n            subsections: null\n"
)


__lowerCamelCase											=  {
    "name": "root",
    "text": "",
    "is_empty_text": True,
    "subsections": [
        {
            "name": "Dataset Card for My Dataset",
            "text": "",
            "is_empty_text": True,
            "subsections": [
                {"name": "Table of Contents", "text": "Some text here.", "is_empty_text": False, "subsections": []},
                {
                    "name": "Dataset Description",
                    "text": "Some text here.",
                    "is_empty_text": False,
                    "subsections": [
                        {
                            "name": "Dataset Summary",
                            "text": "Some text here.",
                            "is_empty_text": False,
                            "subsections": [],
                        },
                        {
                            "name": "Supported Tasks and Leaderboards",
                            "text": "",
                            "is_empty_text": True,
                            "subsections": [],
                        },
                        {"name": "Languages", "text": "Language Text", "is_empty_text": False, "subsections": []},
                    ],
                },
            ],
        }
    ],
}


__lowerCamelCase											=  "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"


__lowerCamelCase											=  "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n#### Extra Ignored Subsection\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

__lowerCamelCase											=  {
    "name": "root",
    "text": "",
    "is_empty_text": True,
    "subsections": [
        {
            "name": "Dataset Card for My Dataset",
            "text": "",
            "is_empty_text": True,
            "subsections": [
                {"name": "Table of Contents", "text": "Some text here.", "is_empty_text": False, "subsections": []},
                {
                    "name": "Dataset Description",
                    "text": "Some text here.",
                    "is_empty_text": False,
                    "subsections": [
                        {
                            "name": "Dataset Summary",
                            "text": "Some text here.",
                            "is_empty_text": False,
                            "subsections": [
                                {
                                    "name": "Extra Ignored Subsection",
                                    "text": "",
                                    "is_empty_text": True,
                                    "subsections": [],
                                }
                            ],
                        },
                        {
                            "name": "Supported Tasks and Leaderboards",
                            "text": "",
                            "is_empty_text": True,
                            "subsections": [],
                        },
                        {"name": "Languages", "text": "Language Text", "is_empty_text": False, "subsections": []},
                    ],
                },
            ],
        }
    ],
}

__lowerCamelCase											=  "\\n---\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

__lowerCamelCase											=  (
    "The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README."
)

__lowerCamelCase											=  "\\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

__lowerCamelCase											=  (
    "The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README."
)

__lowerCamelCase											=  "\\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

__lowerCamelCase											=  "The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README."

__lowerCamelCase											=  "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__lowerCamelCase											=  "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored)."


__lowerCamelCase											=  "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n"
__lowerCamelCase											=  "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found 'None'."

__lowerCamelCase											=  "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Languages\nLanguage Text\n"

__lowerCamelCase											=  "The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`."


__lowerCamelCase											=  "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\n"

__lowerCamelCase											=  "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."

__lowerCamelCase											=  "\\n---\nlanguage:\n- zh\n- en\n---\n\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__lowerCamelCase											=  "The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README."

__lowerCamelCase											=  "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n# Dataset Card My Dataset\n"

__lowerCamelCase											=  "The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README."

__lowerCamelCase											=  "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

__lowerCamelCase											=  "The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."

__lowerCamelCase											=  ""

__lowerCamelCase											=  "The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README."

__lowerCamelCase											=  "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

__lowerCamelCase											=  "The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections."






@pytest.mark.parametrize(
    'readme_md, expected_dict'       ,    [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]       ,    )
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							assert ReadMe.from_string(UpperCamelCase__       ,    UpperCamelCase__				).to_dict() == expected_dict






@pytest.mark.parametrize(
    'readme_md, expected_error'       ,    [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]       ,    )
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							with pytest.raises(UpperCamelCase__       ,    match=re.escape(expected_error.format(path='root'				)				)				):
														A__	     =							ReadMe.from_string(UpperCamelCase__       ,    UpperCamelCase__				)
														readme.validate()






@pytest.mark.parametrize(
    'readme_md, expected_error'       ,    [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]       ,    )
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							with pytest.raises(UpperCamelCase__       ,    match=re.escape(expected_error.format(path='root'				)				)				):
														ReadMe.from_string(UpperCamelCase__       ,    UpperCamelCase__				)






@pytest.mark.parametrize(
    'readme_md,'       ,    [
        (README_MULTIPLE_SAME_HEADING_1),
    ]       ,    )
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							ReadMe.from_string(UpperCamelCase__       ,    UpperCamelCase__       ,    suppress_parsing_errors=UpperCamelCase__				)






@pytest.mark.parametrize(
    'readme_md, expected_dict'       ,    [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]       ,    )
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							with tempfile.TemporaryDirectory() as tmp_dir:
														A__	     =							Path(UpperCamelCase__				) / 'README.md'
														with open(UpperCamelCase__       ,    'w+'				) as readme_file:
																					readme_file.write(UpperCamelCase__				)
														A__	     =							ReadMe.from_readme(UpperCamelCase__       ,    UpperCamelCase__				).to_dict()
														assert out["name"] == path
														assert out["text"] == ""
														assert out["is_empty_text"]
														assert out["subsections"] == expected_dict["subsections"]






@pytest.mark.parametrize(
    'readme_md, expected_error'       ,    [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]       ,    )
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							with tempfile.TemporaryDirectory() as tmp_dir:
														A__	     =							Path(UpperCamelCase__				) / 'README.md'
														with open(UpperCamelCase__       ,    'w+'				) as readme_file:
																					readme_file.write(UpperCamelCase__				)
														A__	     =							expected_error.format(path=UpperCamelCase__				)
														with pytest.raises(UpperCamelCase__       ,    match=re.escape(UpperCamelCase__				)				):
																					A__	     =							ReadMe.from_readme(UpperCamelCase__       ,    UpperCamelCase__				)
																					readme.validate()






@pytest.mark.parametrize(
    'readme_md, expected_error'       ,    [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]       ,    )
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							with tempfile.TemporaryDirectory() as tmp_dir:
														A__	     =							Path(UpperCamelCase__				) / 'README.md'
														with open(UpperCamelCase__       ,    'w+'				) as readme_file:
																					readme_file.write(UpperCamelCase__				)
														A__	     =							expected_error.format(path=UpperCamelCase__				)
														with pytest.raises(UpperCamelCase__       ,    match=re.escape(UpperCamelCase__				)				):
																					ReadMe.from_readme(UpperCamelCase__       ,    UpperCamelCase__				)






@pytest.mark.parametrize(
    'readme_md,'       ,    [
        (README_MULTIPLE_SAME_HEADING_1),
    ]       ,    )
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							with tempfile.TemporaryDirectory() as tmp_dir:
														A__	     =							Path(UpperCamelCase__				) / 'README.md'
														with open(UpperCamelCase__       ,    'w+'				) as readme_file:
																					readme_file.write(UpperCamelCase__				)
														ReadMe.from_readme(UpperCamelCase__       ,    UpperCamelCase__       ,    suppress_parsing_errors=UpperCamelCase__				)





