





"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				UpperCAmelCase						:       Any        =					"""platform"""

				import jax
				import jax.numpy as jnp

				from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a=None ,						a=None  )				->				Dict:



					if attention_mask is None:
										__A     :						List[Any]          =    np.where(input_ids != config.pad_token_id ,						1 ,						0  )
					return {
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}





@require_flax
class 						_A:


				"""simple docstring"""


				def __init__(      self					, _A					, _A=13					, _A=7					, _A=True					, _A=False					, _A=99					, _A=16					, _A=2					, _A=4					, _A=4					, _A="gelu"					, _A=0.1					, _A=0.1					, _A=20					, _A=2					, _A=1					, _A=0					, _A=16					, _A=16					, _A=0.0_2					, ):
									__A     :						Union[str, Any]          =    parent
									__A     :						List[Any]          =    batch_size
									__A     :						List[Any]          =    seq_length
									__A     :						Union[str, Any]          =    is_training
									__A     :						Dict          =    use_labels
									__A     :						Dict          =    vocab_size
									__A     :						Optional[int]          =    hidden_size
									__A     :						List[str]          =    num_hidden_layers
									__A     :						Dict          =    num_attention_heads
									__A     :						Union[str, Any]          =    intermediate_size
									__A     :						List[str]          =    hidden_act
									__A     :						Tuple          =    hidden_dropout_prob
									__A     :						List[str]          =    attention_probs_dropout_prob
									__A     :						Optional[int]          =    max_position_embeddings
									__A     :						str          =    eos_token_id
									__A     :						Any          =    pad_token_id
									__A     :						str          =    bos_token_id
									__A     :						Dict          =    embed_dim
									__A     :						List[Any]          =    word_embed_proj_dim
									__A     :						Union[str, Any]          =    initializer_range
									__A     :						Optional[Any]          =    False



				def  UpperCAmelCase_		(      self					):
									__A     :						Any          =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					, self.vocab_size					)					, 3					, self.vocab_size					)
									__A     :						int          =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					, dtype=np.intaa					))					, -1					)

									__A     :						Union[str, Any]          =    OPTConfig(
									    vocab_size=self.vocab_size					, hidden_size=self.hidden_size					, num_hidden_layers=self.num_hidden_layers					, num_attention_heads=self.num_attention_heads					, ffn_dim=self.intermediate_size					, dropout=self.hidden_dropout_prob					, attention_dropout=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, eos_token_id=self.eos_token_id					, bos_token_id=self.bos_token_id					, pad_token_id=self.pad_token_id					, embed_dim=self.embed_dim					, is_encoder_decoder=lowerCAmelCase__					, word_embed_proj_dim=self.word_embed_proj_dim					, initializer_range=self.initializer_range					, use_cache=lowerCAmelCase__					, )
									__A     :						Optional[int]          =    prepare_opt_inputs_dict(lowerCAmelCase__					, lowerCAmelCase__					)
									return config, inputs_dict



				def  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						List[Any]          =    self.prepare_config_and_inputs()
									return config, inputs_dict



				def  UpperCAmelCase_		(      self					, _A					, _A					, _A					):
									__A     :						Dict          =    20
									__A     :						int          =    model_class_name(lowerCAmelCase__					)

									__A     :						Any          =    inputs_dict['input_ids']
									__A     :						Optional[int]          =    inputs_dict['attention_mask']

									__A     :						Optional[int]          =    model.init_cache(input_ids.shape[0]					, lowerCAmelCase__					)
									__A     :						Optional[Any]          =    jnp.ones((input_ids.shape[0], max_length)					, dtype='i4'					)

									__A     :						Any          =    jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1					)[None, :]					, (input_ids.shape[0], input_ids.shape[-1] - 1)					, )
									__A     :						int          =    model(
									    input_ids[:, :-1]					, attention_mask=lowerCAmelCase__					, past_key_values=lowerCAmelCase__					, position_ids=lowerCAmelCase__					, )

									__A     :						int          =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					, dtype='i4'					)
									__A     :						Optional[Any]          =    model(
									    input_ids[:, -1:]					, attention_mask=lowerCAmelCase__					, past_key_values=outputs_cache.past_key_values					, position_ids=lowerCAmelCase__					, )

									__A     :						Optional[int]          =    model(lowerCAmelCase__					)

									__A     :						Union[str, Any]          =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
									self.parent.assertTrue(diff < 1e-3					, msg=F"""Max diff is {diff}"""					)





				def  UpperCAmelCase_		(      self					, _A					, _A					, _A					):
									__A     :						Any          =    20
									__A     :						Any          =    model_class_name(lowerCAmelCase__					)

									__A   ,    __A     :						Any          =    (
									    inputs_dict['input_ids'],
									    inputs_dict['attention_mask'],
									)

									__A     :						Optional[int]          =    jnp.concatenate(
									    [
									        attention_mask,
									        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])					),
									    ]					, axis=-1					, )

									__A     :						Any          =    model.init_cache(input_ids.shape[0]					, lowerCAmelCase__					)
									__A     :						int          =    jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1					)[None, :]					, (input_ids.shape[0], input_ids.shape[-1] - 1)					, )

									__A     :						Optional[int]          =    model(
									    input_ids[:, :-1]					, attention_mask=lowerCAmelCase__					, past_key_values=lowerCAmelCase__					, position_ids=lowerCAmelCase__					, )
									__A     :						Dict          =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					, dtype='i4'					)
									__A     :						int          =    model(
									    input_ids[:, -1:]					, past_key_values=outputs_cache.past_key_values					, attention_mask=lowerCAmelCase__					, position_ids=lowerCAmelCase__					, )

									__A     :						List[Any]          =    model(lowerCAmelCase__					, attention_mask=lowerCAmelCase__					)

									__A     :						List[Any]          =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
									self.parent.assertTrue(diff < 1e-3					, msg=F"""Max diff is {diff}"""					)





@require_flax
class 						_A( _UpperCAmelCase    ,			unittest.TestCase    ,			_UpperCAmelCase       ):


				"""simple docstring"""


				UpperCamelCase   :  int    =     (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
				UpperCamelCase   :  Dict    =     () if is_flax_available() else ()



				def  UpperCAmelCase_		(      self					):
									__A     :						Optional[int]          =    FlaxOPTModelTester(self					)



				def  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						Any          =    self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward(lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					)



				def  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						List[Any]          =    self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					)





				@slow
				def  UpperCAmelCase_		(      self					):


									for model_class_name in self.all_model_classes:
														__A     :						Optional[Any]          =    model_class_name.from_pretrained('facebook/opt-125m'					)
														__A     :						Union[str, Any]          =    np.ones((1, 1)					) * model.config.eos_token_id
														__A     :						str          =    model(lowerCAmelCase__					)
														self.assertIsNotNone(lowerCAmelCase__					)





@require_sentencepiece
@require_flax
class 						_A( unittest.TestCase       ):


				"""simple docstring"""


				@slow
				def  UpperCAmelCase_		(      self					):
									__A     :						Any          =    FlaxOPTModel.from_pretrained('facebook/opt-350m'					)
									__A     :						List[str]          =    jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					)
									__A     :						Dict          =    model(input_ids=lowerCAmelCase__					).last_hidden_state
									__A     :						Union[str, Any]          =    (1, 11, 512)
									self.assertEqual(output.shape					, lowerCAmelCase__					)
									__A     :						Any          =    jnp.array(
									    [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]					)
									self.assertTrue(jnp.allclose(output[:, :3, :3]					, lowerCAmelCase__					, atol=4e-2					)					)





@require_flax
@slow
class 						_A( unittest.TestCase       ):


				"""simple docstring"""


				def  UpperCAmelCase_		(      self					):
									super().setUp()
									__A     :						Union[str, Any]          =    'facebook/opt-350m'





				def  UpperCAmelCase_		(      self					):
									__A     :						List[str]          =    FlaxOPTForCausalLM.from_pretrained(self.path_model					)
									__A     :						List[Any]          =    GPTaTokenizer.from_pretrained(self.path_model					)

									__A     :						Any          =    [
									    'Today is a beautiful day and I want to',
									    'In the city of',
									    'Paris is the capital of France and',
									    'Computers and mobile phones have taken',
									]
									# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
									__A     :						Dict          =    tokenizer(lowerCAmelCase__					, return_tensors='jax'					, padding=lowerCAmelCase__					, add_special_tokens=lowerCAmelCase__					)
									__A     :						Any          =    model(inputs.input_ids					, attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
									__A     :						Optional[Any]          =    jnp.array(
									    [
									        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
									        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
									        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
									        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
									    ]					)
									self.assertTrue(jnp.allclose(lowerCAmelCase__					, lowerCAmelCase__					, atol=4e-2					)					)

									__A     :						Any          =    jax.jit(lowerCAmelCase__					)
									__A     :						List[str]          =    model(inputs.input_ids					, attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
									self.assertTrue(jnp.allclose(lowerCAmelCase__					, lowerCAmelCase__					, atol=4e-2					)					)





@require_flax
@slow
class 						_A( unittest.TestCase       ):


				"""simple docstring"""


				@property
				def  UpperCAmelCase_		(      self					):
									return [
									    "Today is a beautiful day and I want",
									    "In the city of",
									    "Paris is the capital of France and",
									    "Computers and mobile phones have taken",
									]



				def  UpperCAmelCase_		(      self					):
									__A     :						int          =    'facebook/opt-125m'

									__A     :						List[Any]          =    [
									    'Today is a beautiful day and I want to',
									    'In the city of New York, the city',
									    'Paris is the capital of France and the capital',
									    'Computers and mobile phones have taken over the',
									]

									__A     :						Optional[int]          =    []

									__A     :						List[Any]          =    FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__					)
									__A     :						List[str]          =    GPTaTokenizer.from_pretrained(lowerCAmelCase__					)

									for prompt in self.prompts:
														__A     :						Optional[Any]          =    tokenizer(lowerCAmelCase__					, return_tensors='jax'					).input_ids

														__A     :						str          =    model.generate(lowerCAmelCase__					, max_length=10					)
														__A     :						List[Any]          =    generated_ids[0]

														__A     :						Union[str, Any]          =    tokenizer.batch_decode(lowerCAmelCase__					, skip_special_tokens=lowerCAmelCase__					)
														predicted_outputs += generated_string

									self.assertListEqual(lowerCAmelCase__					, lowerCAmelCase__					)



				def  UpperCAmelCase_		(      self					):
									__A     :						Any          =    'facebook/opt-350m'

									__A     :						List[str]          =    [
									    'Today is a beautiful day and I want to',
									    'In the city of San Francisco, the city',
									    'Paris is the capital of France and the capital',
									    'Computers and mobile phones have taken over the',
									]

									__A     :						int          =    []
									__A     :						Tuple          =    FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__					)
									__A     :						str          =    GPTaTokenizer.from_pretrained(lowerCAmelCase__					)

									for prompt in self.prompts:
														__A     :						List[Any]          =    tokenizer(lowerCAmelCase__					, return_tensors='jax'					).input_ids

														__A     :						Optional[int]          =    model.generate(lowerCAmelCase__					, max_length=10					)
														__A     :						Tuple          =    generated_ids[0]

														__A     :						str          =    tokenizer.batch_decode(lowerCAmelCase__					, skip_special_tokens=lowerCAmelCase__					)
														predicted_outputs += generated_string

									self.assertListEqual(lowerCAmelCase__					, lowerCAmelCase__					)



				def  UpperCAmelCase_		(      self					):
									__A     :						Tuple          =    'facebook/opt-125m'
									__A     :						List[Any]          =    [
									    'Today is a beautiful day and I want to thank',
									    'In the city of Rome Canaver Canaver Canaver Canaver',
									]
									__A     :						Tuple          =    FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__					)
									__A     :						Optional[int]          =    GPTaTokenizer.from_pretrained(lowerCAmelCase__					)
									__A     :						List[Any]          =    tokenizer(
									    [
									        'Today is a beautiful day and I want to',
									        'In the city of',
									    ]					, return_tensors='jax'					, padding=lowerCAmelCase__					, )

									__A     :						Optional[Any]          =    jax.jit(model.generate					)

									__A     :						Any          =    jit_generate(inputs['input_ids']					, attention_mask=inputs['attention_mask']					).sequences

									__A     :						List[Any]          =    tokenizer.batch_decode(lowerCAmelCase__					, skip_special_tokens=lowerCAmelCase__					)

									self.assertIsNotNone(lowerCAmelCase__					, lowerCAmelCase__					)





				def  UpperCAmelCase_		(      self					):
									__A     :						int          =    'facebook/opt-350m'

									__A     :						Any          =    GPTaTokenizer.from_pretrained(lowerCAmelCase__					)
									__A     :						int          =    FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__					)

									__A     :						int          =    'left'

									# use different length sentences to test batching
									__A     :						int          =    [
									    'Hello, my dog is a little',
									    'Today, I',
									]

									__A     :						Optional[int]          =    tokenizer(lowerCAmelCase__					, return_tensors='jax'					, padding=lowerCAmelCase__					)
									__A     :						Tuple          =    inputs['input_ids']

									__A     :						int          =    model.generate(input_ids=lowerCAmelCase__					, attention_mask=inputs['attention_mask']					, trace=lowerCAmelCase__					)

									__A     :						List[str]          =    tokenizer(sentences[0]					, return_tensors='jax'					).input_ids
									__A     :						List[str]          =    model.generate(input_ids=lowerCAmelCase__					)

									__A     :						Optional[Any]          =    inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
									__A     :						List[str]          =    tokenizer(sentences[1]					, return_tensors='jax'					).input_ids
									__A     :						List[Any]          =    model.generate(input_ids=lowerCAmelCase__					, max_length=model.config.max_length - num_paddings					)

									__A     :						Tuple          =    tokenizer.batch_decode(outputs[0]					, skip_special_tokens=lowerCAmelCase__					)
									__A     :						Dict          =    tokenizer.decode(output_non_padded[0][0]					, skip_special_tokens=lowerCAmelCase__					)
									__A     :						Tuple          =    tokenizer.decode(output_padded[0][0]					, skip_special_tokens=lowerCAmelCase__					)

									__A     :						Any          =    [
									    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
									    'Today, I was in the middle of a conversation with a friend about the',
									]
									self.assertListEqual(lowerCAmelCase__					, lowerCAmelCase__					)
									self.assertListEqual(lowerCAmelCase__					, [non_padded_sentence, padded_sentence]					)











import logging
from pathlib import Path
from typing import Any, List, Tuple

import yaml


# loading package files: https://stackoverflow.com/a/20885799
try:
				import importlib.resources as pkg_resources
except ImportError:
				# Try backported to PY<37 `importlib_resources`.
				import importlib_resources as pkg_resources

from . import resources


UpperCAmelCase						:       Optional[Any]        =					'''https://github.com/huggingface/datasets/tree/main/src/datasets/utils'''
UpperCAmelCase						:       Any        =					F"""{BASE_REF_URL}/{__file__}"""
UpperCAmelCase						:       int        =					logging.getLogger(__name__)







def 			_SCREAMING_SNAKE_CASE    (       a  )				->				Tuple[Any, str]:
					__A     :						List[Any]          =    pkg_resources.read_text(a ,						a  )
					return yaml.safe_load(a  ), F"""{BASE_REF_URL}/resources/{resource}"""


UpperCAmelCase  ,			UpperCAmelCase						:       Tuple        =					load_yaml_resource('''readme_structure.yaml''')

UpperCAmelCase						:       List[Any]        =					[
    '''[Needs More Information]''',
    '''[More Information Needed]''',
    '''(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)''',
]

# Dictionary representation of section/readme, error_list, warning_list
UpperCAmelCase						:       Union[str, Any]        =					Tuple[dict, List[str], List[str]]





class 						_A:


				"""simple docstring"""


				def __init__(      self					, _A					, _A					, _A = None					, _A = False					):
									__A     :						Dict          =    name
									__A     :						str          =    level
									__A     :						List[str]          =    lines
									__A     :						List[Any]          =    ''
									__A     :						Optional[Any]          =    True
									__A     :						List[Any]          =    {}
									__A     :						Optional[Any]          =    []
									__A     :						Optional[int]          =    []
									if self.lines is not None:
														self.parse(suppress_parsing_errors=_A					)



				def  UpperCAmelCase_		(      self					, _A = False					):
									__A     :						Any          =    ''
									__A     :						Union[str, Any]          =    []
									__A     :						Tuple          =    False
									for line in self.lines:
														if line.strip(' \n'					) == "":
																			continue
														elif line.strip(' \n'					)[:3] == "```":
																			__A     :						int          =    not code_start
														elif line.split()[0] == self.level + "#" and not code_start:
																			if current_sub_level != "":
																								__A     :						Dict          =    Section(_A					, self.level + '#'					, _A					)
																								__A     :						Optional[int]          =    []
																			else:
																								if current_lines != []:
																													self.text += "".join(_A					).strip()
																													if self.text != "" and self.text not in FILLER_TEXT:
																																		__A     :						Union[str, Any]          =    False
																													__A     :						Dict          =    []

																			__A     :						Optional[Any]          =    ' '.join(line.split()[1:]					).strip(' \n'					)
														else:
																			current_lines.append(_A					)
									else:
														if current_sub_level != "":
																			if current_sub_level in self.content:
																								self.parsing_error_list.append(
																								    F"""Multiple sections with the same heading `{current_sub_level}` have been found. Please keep only one of these sections."""					)
																			__A     :						Any          =    Section(_A					, self.level + '#'					, _A					)
														else:
																			if current_lines != []:
																								self.text += "".join(_A					).strip()
																								if self.text != "" and self.text not in FILLER_TEXT:
																													__A     :						str          =    False

									if self.level == "" and not suppress_parsing_errors:
														if self.parsing_error_list != [] or self.parsing_warning_list != []:
																			__A     :						Optional[int]          =    '\n'.join('-\t' + x for x in self.parsing_error_list + self.parsing_warning_list					)
																			__A     :						List[Any]          =    F"""The following issues were found while parsing the README at `{self.name}`:\n""" + errors
																			raise ValueError(_A					)



				def  UpperCAmelCase_		(      self					, _A					):
									__A     :						Any          =    []
									__A     :						Tuple          =    []
									if structure["allow_empty"] is False:
														# If content is expected
														if self.is_empty_text and self.content == {}:
																			# If no content is found, mention it in the error_list
																			error_list.append(F"""Expected some content in section `{self.name}` but it is empty."""					)

									if structure["allow_empty_text"] is False:
														# If some text is expected
														if self.is_empty_text:
																			# If no text is found, mention it in the error_list
																			error_list.append(
																			    F"""Expected some text in section `{self.name}` but it is empty (text in subsections are ignored)."""					)
        # Subsections Validation
									if structure["subsections"] is not None:
														# If subsections are expected
														if self.content == {}:
																			# If no subsections are present
																			__A     :						int          =    [subsection['name'] for subsection in structure['subsections']]
																			# Mention the expected values in the error_list
																			error_list.append(
																			    F"""Section `{self.name}` expected the following subsections: {", ".join(["`"+x+"`" for x in values]					)}. Found 'None'."""					)
														else:
																			# If some subsections are present
																			__A     :						Optional[Any]          =    [subsection['name'] for subsection in structure['subsections']]
																			__A     :						str          =    False
																			for idx, name in enumerate(_A					):
																								if name not in self.content:
																													# If the expected subsection is not present
																													error_list.append(F"""Section `{self.name}` is missing subsection: `{name}`."""					)
																													__A     :						Optional[Any]          =    True
																								else:
																													# If the subsection is present, validate subsection, return the result
																													# and concat the errors from subsection to section error_list

																													# Skip sublevel validation if current level is `###`
																													if self.level == "###":
																																		continue
																													else:
																																		__A   ,    __A   ,    __A     :						Optional[int]          =    self.content[name].validate(
																																		    structure['subsections'][idx]					)
																													error_list += subsec_error_list
																													warning_list += subsec_warning_list

																			if has_missing_subsections:  # we only allow to have extra subsections if all the other ones are here
																								for name in self.content:
																													if name not in structure_names:
																																		# If an extra subsection is present
																																		warning_list.append(
																																		    F"""`{self.name}` has an extra subsection: `{name}`. Skipping further validation checks for this subsection as expected structure is unknown."""					)
									if error_list:
														# If there are errors, do not return the dictionary as it is invalid
														return {}, error_list, warning_list
									else:
														return self.to_dict(), error_list, warning_list





				def  UpperCAmelCase_		(      self					):
									return {
									    "name": self.name,
									    "text": self.text,
									    "is_empty_text": self.is_empty_text,
									    "subsections": [value.to_dict() for value in self.content.values()],
									}





class 						_A( snake_case__       ):  # Level 0


				"""simple docstring"""


				def __init__(      self					, _A					, _A					, _A = None					, _A = False					):
									super().__init__(name=_A					, level=''					)  # Not using lines here as we need to use a child class parse
									__A     :						List[str]          =    structure
									__A     :						str          =    -2
									__A     :						Tuple          =    0
									__A     :						Dict          =    lines
									if self.lines is not None:
														self.parse(suppress_parsing_errors=_A					)



				def  UpperCAmelCase_		(      self					):
									if self.structure is None:
														__A   ,    __A   ,    __A     :						List[Any]          =    self._validate(_A					)
									else:
														__A   ,    __A   ,    __A     :						Any          =    self._validate(self.structure					)
									if error_list != [] or warning_list != []:
														__A     :						Optional[Any]          =    '\n'.join(['-\t' + x for x in error_list + warning_list]					)
														__A     :						Union[str, Any]          =    F"""The following issues were found for the README at `{self.name}`:\n""" + errors
														raise ValueError(_A					)



				@classmethod
				def  UpperCAmelCase_		(      cls					, _A					, _A = None					, _A = False					):
									with open(_A					, encoding='utf-8'					) as f:
														__A     :						str          =    f.readlines()
									return cls(_A					, _A					, _A					, suppress_parsing_errors=_A					)



				@classmethod
				def  UpperCAmelCase_		(      cls					, _A					, _A = None					, _A = "root"					, _A = False					):
									__A     :						Any          =    string.split('\n'					)
									return cls(_A					, _A					, _A					, suppress_parsing_errors=_A					)



				def  UpperCAmelCase_		(      self					, _A = False					):
									# Skip Tags
									__A     :						Any          =    0

									for line in self.lines:
														self.yaml_tags_line_count += 1
														if line.strip(' \n'					) == "---":
																			self.tag_count += 1
																			if self.tag_count == 2:
																								break
														line_count += 1
									if self.tag_count == 2:
														__A     :						List[str]          =    self.lines[line_count + 1 :]  # Get the last + 1 th item.
									else:
														__A     :						List[Any]          =    self.lines[self.tag_count :]
									super().parse(suppress_parsing_errors=_A					)



				def __str__(      self					):
									return str(self.to_dict()					)





				def  UpperCAmelCase_		(      self					, _A					):
									__A     :						List[str]          =    []
									__A     :						List[Any]          =    []
									if self.yaml_tags_line_count == 0:
														warning_list.append('Empty YAML markers are present in the README.'					)
									elif self.tag_count == 0:
														warning_list.append('No YAML markers are present in the README.'					)
									elif self.tag_count == 1:
														warning_list.append('Only the start of YAML tags present in the README.'					)
									# Check how many first level sections are present.
									__A     :						Optional[int]          =    len(self.content.keys()					)
									if num_first_level_keys > 1:
														# If more than one, add to the error list, continue
														error_list.append(
														    F"""The README has several first-level headings: {", ".join(["`"+x+"`" for x in list(self.content.keys()					)]					)}. Only one heading is expected. Skipping further validation for this README."""					)
									elif num_first_level_keys < 1:
														# If less than one, append error.
														error_list.append(
														    'The README has no first-level headings. One heading is expected. Skipping further validation for this README.'					)

									else:
														# If one exactly
														__A     :						List[Any]          =    list(self.content.keys()					)[0]  # Get the key
														if start_key.startswith('Dataset Card for'					):  # Check correct start
																			# If the starting is correct, validate all the sections
																			__A   ,    __A   ,    __A     :						List[str]          =    self.content[start_key].validate(
																			    readme_structure['subsections'][0]					)
																			error_list += sec_error_list
																			warning_list += sec_warning_list
														else:
																			# If not found, append error
																			error_list.append(
																			    'No first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'					)
									if error_list:
														# If there are errors, do not return the dictionary as it is invalid
														return {}, error_list, warning_list
									else:
														return self.to_dict(), error_list, warning_list


if __name__ == "__main__":
				from argparse import ArgumentParser

				UpperCAmelCase						:       Dict        =					ArgumentParser(usage='''Validate the content (excluding YAML tags) of a README.md file.''')
				ap.add_argument('''readme_filepath''')
				UpperCAmelCase						:       Tuple        =					ap.parse_args()
				UpperCAmelCase						:       List[str]        =					Path(args.readme_filepath)
				UpperCAmelCase						:       int        =					ReadMe.from_readme(readme_filepath)




