






import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

__a      :       Dict				  =   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"
)


__a      :       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": "Supported Tasks and Leaderboards",
                            "text": "",
                            "is_empty_text": True,
                            "subsections": [],
                        },
                        {"name": "Languages", "text": "Language Text", "is_empty_text": False, "subsections": []},
                    ],
                },
            ],
        }
    ],
}


__a      :       List[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"


__a      :       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"

__a      :       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": []},
                    ],
                },
            ],
        }
    ],
}

__a      :       str				  =   "\\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"

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

__a      :       int				  =   "\\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"

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

__a      :       str				  =   "\\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"

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

__a      :       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\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__a      :       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)."


__a      :       int				  =   "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n"
__a      :       int				  =   "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'."

__a      :       List[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### Languages\nLanguage Text\n"

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


__a      :       List[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\n"

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

__a      :       Tuple				  =   "\\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"
__a      :       Optional[Any]				  =   "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."

__a      :       Union[str, 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# Dataset Card My Dataset\n"

__a      :       List[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."

__a      :       Tuple				  =   "\\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"

__a      :       List[str]				  =   "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."

__a      :       List[Any]				  =   ""

__a      :       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."

__a      :       str				  =   "\\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"

__a      :       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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Union[str, Any]			,				__lowercase   :			Tuple    )						->      Tuple:





				"""simple docstring"""

				assert ReadMe.from_string(_snake_case			,				_snake_case    ).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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			Dict    )						->      Optional[int]:





				"""simple docstring"""

				with pytest.raises(_snake_case			,				match=re.escape(expected_error.format(path="""root"""    )    )    ):
								__A         =	ReadMe.from_string(_snake_case			,				_snake_case    )
								readme.validate()



@pytest.mark.parametrize(
    """readme_md, expected_error"""			,				[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any			,				__lowercase   :			Any    )						->      Tuple:





				"""simple docstring"""

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



@pytest.mark.parametrize(
    """readme_md,"""			,				[
        (README_MULTIPLE_SAME_HEADING_1),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Union[str, Any]    )						->      Union[str, Any]:





				"""simple docstring"""

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



@pytest.mark.parametrize(
    """readme_md, expected_dict"""			,				[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any			,				__lowercase   :			Dict    )						->      Tuple:





				"""simple docstring"""

				with tempfile.TemporaryDirectory() as tmp_dir:
								__A         =	Path(_snake_case    ) / """README.md"""
								with open(_snake_case			,				"""w+"""    ) as readme_file:
												readme_file.write(_snake_case    )
								__A         =	ReadMe.from_readme(_snake_case			,				_snake_case    ).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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Union[str, Any]			,				__lowercase   :			Optional[Any]    )						->      int:





				"""simple docstring"""

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



@pytest.mark.parametrize(
    """readme_md, expected_error"""			,				[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any			,				__lowercase   :			List[Any]    )						->      Dict:





				"""simple docstring"""

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






@pytest.mark.parametrize(
    """readme_md,"""			,				[
        (README_MULTIPLE_SAME_HEADING_1),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple    )						->      str:





				"""simple docstring"""

				with tempfile.TemporaryDirectory() as tmp_dir:
								__A         =	Path(_snake_case    ) / """README.md"""
								with open(_snake_case			,				"""w+"""    ) as readme_file:
												readme_file.write(_snake_case    )
								ReadMe.from_readme(_snake_case			,				_snake_case			,				suppress_parsing_errors=_snake_case    )












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       __lowercase      :







     '''simple docstring'''




     def __init__(				self       :			Dict ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			Any=13 ,	UpperCamelCase_       :			int=7 ,	UpperCamelCase_       :			List[str]=True ,	UpperCamelCase_       :			Optional[int]=True ,	UpperCamelCase_       :			Optional[int]=False ,	UpperCamelCase_       :			Dict=True ,	UpperCamelCase_       :			Any=99 ,	UpperCamelCase_       :			Dict=32 ,	UpperCamelCase_       :			str=5 ,	UpperCamelCase_       :			Dict=4 ,	UpperCamelCase_       :			str=37 ,	UpperCamelCase_       :			List[Any]="gelu" ,	UpperCamelCase_       :			Dict=0.1 ,	UpperCamelCase_       :			List[str]=0.1 ,	UpperCamelCase_       :			List[Any]=512 ,	UpperCamelCase_       :			Optional[Any]=16 ,	UpperCamelCase_       :			Dict=2 ,	UpperCamelCase_       :			Any=0.02 ,	UpperCamelCase_       :			Tuple=3 ,	UpperCamelCase_       :			Optional[int]=4 ,	UpperCamelCase_       :			str=None ,	):


         """simple docstring"""



         __A         =	parent
         __A         =	batch_size
         __A         =	seq_length
         __A         =	is_training
         __A         =	use_input_mask
         __A         =	use_token_type_ids
         __A         =	use_labels
         __A         =	vocab_size
         __A         =	hidden_size
         __A         =	num_hidden_layers
         __A         =	num_attention_heads
         __A         =	intermediate_size
         __A         =	hidden_act
         __A         =	hidden_dropout_prob
         __A         =	attention_probs_dropout_prob
         __A         =	max_position_embeddings
         __A         =	type_vocab_size
         __A         =	type_sequence_label_size
         __A         =	initializer_range
         __A         =	num_labels
         __A         =	num_choices
         __A         =	scope




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size				)

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

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

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

         __A         =	self.get_config()

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




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         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=UpperCamelCase_ ,	initializer_range=self.initializer_range ,	)




     def 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Any				):


         """simple docstring"""



         __A         =	LlamaModel(config=UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_				)
         __A         =	model(UpperCamelCase_				)
         self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)




     def 		lowerCAmelCase_     (				self       :			Dict ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Tuple ,	):


         """simple docstring"""



         __A         =	True
         __A         =	LlamaModel(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	encoder_attention_mask=UpperCamelCase_ ,	)
         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	)
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_				)
         self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)




     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str ,	):


         """simple docstring"""



         __A         =	LlamaForCausalLM(config=UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	labels=UpperCamelCase_				)
         self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.seq_length, self.vocab_size)				)




     def 		lowerCAmelCase_     (				self       :			Dict ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Union[str, Any] ,	):


         """simple docstring"""



         __A         =	True
         __A         =	True
         __A         =	LlamaForCausalLM(config=UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()

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

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

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

         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	encoder_attention_mask=UpperCamelCase_ ,	output_hidden_states=UpperCamelCase_ ,	)["""hidden_states"""][0]
         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	encoder_attention_mask=UpperCamelCase_ ,	past_key_values=UpperCamelCase_ ,	output_hidden_states=UpperCamelCase_ ,	)["""hidden_states"""][0]

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





     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	self.prepare_config_and_inputs()
         (
             (
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	
         )         =	config_and_inputs
         __A         =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict


@require_torch
class       __lowercase      (	lowercase_			,      lowercase_			,      lowercase_			,      unittest.TestCase  ):







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




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	LlamaModelTester(self				)
         __A         =	ConfigTester(self ,	config_class=UpperCamelCase_ ,	hidden_size=37				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             __A         =	type
             self.model_tester.create_and_check_model(*UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	3
         __A         =	input_dict["""input_ids"""]
         __A         =	input_ids.ne(1				).to(UpperCamelCase_				)
         __A         =	ids_tensor([self.model_tester.batch_size] ,	self.model_tester.type_sequence_label_size				)
         __A         =	LlamaForSequenceClassification(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	labels=UpperCamelCase_				)
         self.assertEqual(result.logits.shape ,	(self.model_tester.batch_size, self.model_tester.num_labels)				)




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	3
         __A         =	"""single_label_classification"""
         __A         =	input_dict["""input_ids"""]
         __A         =	input_ids.ne(1				).to(UpperCamelCase_				)
         __A         =	ids_tensor([self.model_tester.batch_size] ,	self.model_tester.type_sequence_label_size				)
         __A         =	LlamaForSequenceClassification(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	labels=UpperCamelCase_				)
         self.assertEqual(result.logits.shape ,	(self.model_tester.batch_size, self.model_tester.num_labels)				)




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	3
         __A         =	"""multi_label_classification"""
         __A         =	input_dict["""input_ids"""]
         __A         =	input_ids.ne(1				).to(UpperCamelCase_				)
         __A         =	ids_tensor(
             [self.model_tester.batch_size, config.num_labels] ,	self.model_tester.type_sequence_label_size				).to(torch.float				)
         __A         =	LlamaForSequenceClassification(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	labels=UpperCamelCase_				)
         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       :			Tuple				):


         """simple docstring"""



         pass





     @parameterized.expand([("""linear""",), ("""dynamic""",)]				)
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			Optional[Any]				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	ids_tensor([1, 10] ,	config.vocab_size				)
         __A         =	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
         __A         =	LlamaModel(UpperCamelCase_				)
         original_model.to(UpperCamelCase_				)
         original_model.eval()
         __A         =	original_model(UpperCamelCase_				).last_hidden_state
         __A         =	original_model(UpperCamelCase_				).last_hidden_state

         set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
         __A         =	{"""type""": scaling_type, """factor""": 10.0}
         __A         =	LlamaModel(UpperCamelCase_				)
         scaled_model.to(UpperCamelCase_				)
         scaled_model.eval()
         __A         =	scaled_model(UpperCamelCase_				).last_hidden_state
         __A         =	scaled_model(UpperCamelCase_				).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(UpperCamelCase_ ,	UpperCamelCase_ ,	atol=1e-5				)				)
         else:
             self.assertFalse(torch.allclose(UpperCamelCase_ ,	UpperCamelCase_ ,	atol=1e-5				)				)

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


@require_torch
class       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""				)
     @slow
     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor([input_ids]				)				)
         # Expected mean on dim = -1
         __A         =	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				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A         =	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] ,	UpperCamelCase_ ,	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       :			str				):


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor(UpperCamelCase_				)				)
         # Expected mean on dim = -1
         __A         =	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				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A         =	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] ,	UpperCamelCase_ ,	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       :			Tuple				):


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor(UpperCamelCase_				)				)
         # Expected mean on dim = -1
         __A         =	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				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A         =	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				) ,	UpperCamelCase_ ,	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       :			Tuple				):


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor(UpperCamelCase_				)				)

         __A         =	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				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # fmt: off
         __A         =	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] ,	UpperCamelCase_ ,	atol=1e-5 ,	rtol=1e-5				)





     @unittest.skip("""Model is curently gated"""				)
     @slow
     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	"""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"""
         __A         =	"""Simply put, the theory of relativity states that """
         __A         =	LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""				)
         __A         =	tokenizer.encode(UpperCamelCase_ ,	return_tensors="""pt"""				)
         __A         =	LlamaForCausalLM.from_pretrained(
             """meta-llama/Llama-2-13b-chat-hf""" ,	device_map="""sequential""" ,	use_safetensors=UpperCamelCase_				)

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




