




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__ :   Any					=							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__ :   Tuple					=							{
    '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__ :   Any					=							'\\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__ :   Optional[Any]					=							'\\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__ :   Optional[int]					=							{
    '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__ :   Tuple					=							'\\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__ :   Optional[int]					=							(
    'The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README.'
)

lowerCamelCase__ :   str					=							'\\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__ :   Union[str, Any]					=							(
    'The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README.'
)

lowerCamelCase__ :   Any					=							'\\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__ :   str					=							'The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README.'

lowerCamelCase__ :   Any					=							'\\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__ :   Optional[int]					=							'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__ :   Dict					=							'\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n'
lowerCamelCase__ :   str					=							'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__ :   int					=							'\\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__ :   Union[str, Any]					=							'The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`.'


lowerCamelCase__ :   Optional[int]					=							'\\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__ :   Dict					=							'The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty.'

lowerCamelCase__ :   List[str]					=							'\\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__ :   Optional[int]					=							'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__ :   int					=							'\\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__ :   Optional[Any]					=							'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__ :   List[str]					=							'\\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__ :   List[Any]					=							'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__ :   str					=							''

lowerCamelCase__ :   str					=							'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__ :   List[Any]					=							'\\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__ :   Dict					=							'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						:	Optional[int]			,			__UpperCAmelCase						:	str							)					-> Optional[int]:
      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						:	List[Any]			,			__UpperCAmelCase						:	Dict							)					-> int:
      with pytest.raises(__UpperCAmelCase			,			match=re.escape(expected_error.format(path='root'							)							)							):
            SCREAMING_SNAKE_CASE_							=			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						:	Optional[int]			,			__UpperCAmelCase						:	Optional[int]							)					-> Dict:
      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						:	List[Any]							)					-> List[Any]:
      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						:	Tuple			,			__UpperCAmelCase						:	Optional[int]							)					-> Optional[int]:
      with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE_							=			Path(__UpperCAmelCase							) / 'README.md'
            with open(__UpperCAmelCase			,			'w+'							) as readme_file:
                  readme_file.write(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_							=			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						:	Optional[int]			,			__UpperCAmelCase						:	str							)					-> List[Any]:
      with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE_							=			Path(__UpperCAmelCase							) / 'README.md'
            with open(__UpperCAmelCase			,			'w+'							) as readme_file:
                  readme_file.write(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_							=			expected_error.format(path=__UpperCAmelCase							)
            with pytest.raises(__UpperCAmelCase			,			match=re.escape(__UpperCAmelCase							)							):
                  SCREAMING_SNAKE_CASE_							=			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						:	Dict			,			__UpperCAmelCase						:	List[str]							)					-> List[str]:
      with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE_							=			Path(__UpperCAmelCase							) / 'README.md'
            with open(__UpperCAmelCase			,			'w+'							) as readme_file:
                  readme_file.write(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_							=			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						:	Optional[Any]							)					-> List[str]:
      with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE_							=			Path(__UpperCAmelCase							) / 'README.md'
            with open(__UpperCAmelCase			,			'w+'							) as readme_file:
                  readme_file.write(__UpperCAmelCase							)
            ReadMe.from_readme(__UpperCAmelCase			,			__UpperCAmelCase			,			suppress_parsing_errors=__UpperCAmelCase							)





import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Tuple=13				,				_lowerCAmelCase  :		List[str]=7				,				_lowerCAmelCase  :		Dict=True				,				_lowerCAmelCase  :		Dict=True				,				_lowerCAmelCase  :		Optional[int]=False				,				_lowerCAmelCase  :		Any=True				,				_lowerCAmelCase  :		str=99				,				_lowerCAmelCase  :		List[str]=32				,				_lowerCAmelCase  :		Tuple=5				,				_lowerCAmelCase  :		List[str]=4				,				_lowerCAmelCase  :		str=37				,				_lowerCAmelCase  :		Any="gelu"				,				_lowerCAmelCase  :		str=0.1				,				_lowerCAmelCase  :		Union[str, Any]=0.1				,				_lowerCAmelCase  :		Optional[int]=512				,				_lowerCAmelCase  :		Union[str, Any]=16				,				_lowerCAmelCase  :		Tuple=2				,				_lowerCAmelCase  :		Tuple=0.02				,				_lowerCAmelCase  :		Tuple=3				,				_lowerCAmelCase  :		Dict=4				,				_lowerCAmelCase  :		Union[str, Any]=None				,				):
       SCREAMING_SNAKE_CASE_							=			parent
       SCREAMING_SNAKE_CASE_							=			batch_size
       SCREAMING_SNAKE_CASE_							=			seq_length
       SCREAMING_SNAKE_CASE_							=			is_training
       SCREAMING_SNAKE_CASE_							=			use_input_mask
       SCREAMING_SNAKE_CASE_							=			use_token_type_ids
       SCREAMING_SNAKE_CASE_							=			use_labels
       SCREAMING_SNAKE_CASE_							=			vocab_size
       SCREAMING_SNAKE_CASE_							=			hidden_size
       SCREAMING_SNAKE_CASE_							=			num_hidden_layers
       SCREAMING_SNAKE_CASE_							=			num_attention_heads
       SCREAMING_SNAKE_CASE_							=			intermediate_size
       SCREAMING_SNAKE_CASE_							=			hidden_act
       SCREAMING_SNAKE_CASE_							=			hidden_dropout_prob
       SCREAMING_SNAKE_CASE_							=			attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_							=			max_position_embeddings
       SCREAMING_SNAKE_CASE_							=			type_vocab_size
       SCREAMING_SNAKE_CASE_							=			type_sequence_label_size
       SCREAMING_SNAKE_CASE_							=			initializer_range
       SCREAMING_SNAKE_CASE_							=			num_labels
       SCREAMING_SNAKE_CASE_							=			num_choices
       SCREAMING_SNAKE_CASE_							=			scope



 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size						)

       SCREAMING_SNAKE_CASE_							=			None
       if self.use_input_mask:
             SCREAMING_SNAKE_CASE_							=			random_attention_mask([self.batch_size, self.seq_length]						)

       SCREAMING_SNAKE_CASE_							=			None
       if self.use_token_type_ids:
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.type_vocab_size						)

       SCREAMING_SNAKE_CASE_							=			None
       SCREAMING_SNAKE_CASE_							=			None
       SCREAMING_SNAKE_CASE_							=			None
       if self.use_labels:
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size]				,				self.type_sequence_label_size						)
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.num_labels						)
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size]				,				self.num_choices						)

       SCREAMING_SNAKE_CASE_							=			self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       return LlamaConfig(
           vocab_size=self.vocab_size				,				hidden_size=self.hidden_size				,				num_hidden_layers=self.num_hidden_layers				,				num_attention_heads=self.num_attention_heads				,				intermediate_size=self.intermediate_size				,				hidden_act=self.hidden_act				,				hidden_dropout_prob=self.hidden_dropout_prob				,				attention_probs_dropout_prob=self.attention_probs_dropout_prob				,				max_position_embeddings=self.max_position_embeddings				,				type_vocab_size=self.type_vocab_size				,				is_decoder=_lowerCAmelCase				,				initializer_range=self.initializer_range				,				)



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			LlamaModel(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)						)



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		Optional[int]				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		List[str]				,				):
       SCREAMING_SNAKE_CASE_							=			True
       SCREAMING_SNAKE_CASE_							=			LlamaModel(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(
           _lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				encoder_hidden_states=_lowerCAmelCase				,				encoder_attention_mask=_lowerCAmelCase				,				)
       SCREAMING_SNAKE_CASE_							=			model(
           _lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				encoder_hidden_states=_lowerCAmelCase				,				)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)						)



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		str				,				):
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)						)



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		List[str]				,				):
       SCREAMING_SNAKE_CASE_							=			True
       SCREAMING_SNAKE_CASE_							=			True
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()

       # first forward pass
       SCREAMING_SNAKE_CASE_							=			model(
           _lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				encoder_hidden_states=_lowerCAmelCase				,				encoder_attention_mask=_lowerCAmelCase				,				use_cache=_lowerCAmelCase				,				)
       SCREAMING_SNAKE_CASE_							=			outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE_							=			ids_tensor((self.batch_size, 3)				,				config.vocab_size						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor((self.batch_size, 3)				,				vocab_size=2						)

       # append to next input_ids and
       SCREAMING_SNAKE_CASE_							=			torch.cat([input_ids, next_tokens]				,				dim=-1						)
       SCREAMING_SNAKE_CASE_							=			torch.cat([input_mask, next_mask]				,				dim=-1						)

       SCREAMING_SNAKE_CASE_							=			model(
           _lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				encoder_hidden_states=_lowerCAmelCase				,				encoder_attention_mask=_lowerCAmelCase				,				output_hidden_states=_lowerCAmelCase				,				)['hidden_states'][0]
       SCREAMING_SNAKE_CASE_							=			model(
           _lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				encoder_hidden_states=_lowerCAmelCase				,				encoder_attention_mask=_lowerCAmelCase				,				past_key_values=_lowerCAmelCase				,				output_hidden_states=_lowerCAmelCase				,				)['hidden_states'][0]

       # select random slice
       SCREAMING_SNAKE_CASE_							=			ids_tensor((1,)				,				output_from_past.shape[-1]						).item()
       SCREAMING_SNAKE_CASE_							=			output_from_no_past[:, -3:, random_slice_idx].detach()
       SCREAMING_SNAKE_CASE_							=			output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]						)

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(_lowerCAmelCase				,				_lowerCAmelCase				,				atol=1E-3						)						)




 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, 
       )							=			config_and_inputs
       SCREAMING_SNAKE_CASE_							=			{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict





@require_torch
class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   unittest.TestCase					):


 '''simple docstring'''







 lowercase_				    =    (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
 lowercase_				    =    (LlamaForCausalLM,) if is_torch_available() else ()
 lowercase_				    =    (
     {
         "feature-extraction": LlamaModel,
         "text-classification": LlamaForSequenceClassification,
         "text-generation": LlamaForCausalLM,
         "zero-shot": LlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 lowercase_				    =    False
 lowercase_				    =    False



 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			LlamaModelTester(self						)
       SCREAMING_SNAKE_CASE_							=			ConfigTester(self				,				config_class=_lowerCAmelCase				,				hidden_size=37						)



 def       lowerCAmelCase_  (							self  :		Any						):
       self.config_tester.run_common_tests()



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
             SCREAMING_SNAKE_CASE_							=			type
             self.model_tester.create_and_check_model(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			3
       SCREAMING_SNAKE_CASE_							=			input_dict['input_ids']
       SCREAMING_SNAKE_CASE_							=			input_ids.ne(1						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor([self.model_tester.batch_size]				,				self.model_tester.type_sequence_label_size						)
       SCREAMING_SNAKE_CASE_							=			LlamaForSequenceClassification(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.assertEqual(result.logits.shape				,				(self.model_tester.batch_size, self.model_tester.num_labels)						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			3
       SCREAMING_SNAKE_CASE_							=			'single_label_classification'
       SCREAMING_SNAKE_CASE_							=			input_dict['input_ids']
       SCREAMING_SNAKE_CASE_							=			input_ids.ne(1						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor([self.model_tester.batch_size]				,				self.model_tester.type_sequence_label_size						)
       SCREAMING_SNAKE_CASE_							=			LlamaForSequenceClassification(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.assertEqual(result.logits.shape				,				(self.model_tester.batch_size, self.model_tester.num_labels)						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			3
       SCREAMING_SNAKE_CASE_							=			'multi_label_classification'
       SCREAMING_SNAKE_CASE_							=			input_dict['input_ids']
       SCREAMING_SNAKE_CASE_							=			input_ids.ne(1						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor(
           [self.model_tester.batch_size, config.num_labels]				,				self.model_tester.type_sequence_label_size						).to(torch.float						)
       SCREAMING_SNAKE_CASE_							=			LlamaForSequenceClassification(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.assertEqual(result.logits.shape				,				(self.model_tester.batch_size, self.model_tester.num_labels)						)



 @unittest.skip('LLaMA buffers include complex numbers, which breaks this test'						)
 def       lowerCAmelCase_  (							self  :		int						):
       pass




 @parameterized.expand([('linear',), ('dynamic',)]						)
 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			ids_tensor([1, 10]				,				config.vocab_size						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor([1, int(config.max_position_embeddings * 1.5						)]				,				config.vocab_size						)

       set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
       SCREAMING_SNAKE_CASE_							=			LlamaModel(_lowerCAmelCase						)
       original_model.to(_lowerCAmelCase						)
       original_model.eval()
       SCREAMING_SNAKE_CASE_							=			original_model(_lowerCAmelCase						).last_hidden_state
       SCREAMING_SNAKE_CASE_							=			original_model(_lowerCAmelCase						).last_hidden_state

       set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
       SCREAMING_SNAKE_CASE_							=			{'type': scaling_type, 'factor': 10.0}
       SCREAMING_SNAKE_CASE_							=			LlamaModel(_lowerCAmelCase						)
       scaled_model.to(_lowerCAmelCase						)
       scaled_model.eval()
       SCREAMING_SNAKE_CASE_							=			scaled_model(_lowerCAmelCase						).last_hidden_state
       SCREAMING_SNAKE_CASE_							=			scaled_model(_lowerCAmelCase						).last_hidden_state

       # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
       # maximum sequence length, so the outputs for the short input should match.
       if scaling_type == "dynamic":
             self.assertTrue(torch.allclose(_lowerCAmelCase				,				_lowerCAmelCase				,				atol=1E-5						)						)
       else:
             self.assertFalse(torch.allclose(_lowerCAmelCase				,				_lowerCAmelCase				,				atol=1E-5						)						)

       # The output should be different for long inputs
       self.assertFalse(torch.allclose(_lowerCAmelCase				,				_lowerCAmelCase				,				atol=1E-5						)						)





@require_torch
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'						)
 @slow
 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor([input_ids]						)						)
       # Expected mean on dim = -1
       SCREAMING_SNAKE_CASE_							=			torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]						)
       torch.testing.assert_close(out.mean(-1						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]						)
       # fmt: on
       torch.testing.assert_close(out[0, 0, :30]				,				_lowerCAmelCase				,				atol=1E-5				,				rtol=1E-5						)



 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'						)
 @slow
 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor(_lowerCAmelCase						)						)
       # Expected mean on dim = -1
       SCREAMING_SNAKE_CASE_							=			torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]						)
       torch.testing.assert_close(out.mean(-1						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]						)
       # fmt: on
       torch.testing.assert_close(out[0, 0, :30]				,				_lowerCAmelCase				,				atol=1E-5				,				rtol=1E-5						)



 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'						)
 @slow
 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor(_lowerCAmelCase						)						)
       # Expected mean on dim = -1
       SCREAMING_SNAKE_CASE_							=			torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]						)
       torch.testing.assert_close(out.mean(-1						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]						)
       # fmt: on
       torch.testing.assert_close(out.mean(-1						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)



 @unittest.skip(
     'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'						)
 @slow
 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor(_lowerCAmelCase						)						)

       SCREAMING_SNAKE_CASE_							=			torch.tensor(
           [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]				,				dtype=torch.floataa						)
       torch.testing.assert_close(out.mean(-1						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]						)
       # fmt: on
       torch.testing.assert_close(out[0, 0, :30]				,				_lowerCAmelCase				,				atol=1E-5				,				rtol=1E-5						)




 @unittest.skip('Model is curently gated'						)
 @slow
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
       SCREAMING_SNAKE_CASE_							=			'Simply put, the theory of relativity states that '
       SCREAMING_SNAKE_CASE_							=			LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'						)
       SCREAMING_SNAKE_CASE_							=			tokenizer.encode(_lowerCAmelCase				,				return_tensors='pt'						)
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained(
           'meta-llama/Llama-2-13b-chat-hf'				,				device_map='sequential'				,				use_safetensors=_lowerCAmelCase						)

       # greedy generation outputs
       SCREAMING_SNAKE_CASE_							=			model.generate(_lowerCAmelCase				,				max_new_tokens=64				,				top_p=_lowerCAmelCase				,				temperature=1				,				do_sample=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			tokenizer.decode(generated_ids[0]				,				skip_special_tokens=_lowerCAmelCase						)
       self.assertEqual(_lowerCAmelCase				,				_lowerCAmelCase						)