



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.layoutlm.modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMModel,
    )



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=13,			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=99,			SCREAMING_SNAKE_CASE_=32,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=37,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=512,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=1000,			)     ->		Any:
          UpperCamelCase    :  Optional[int]							=	parent
          UpperCamelCase    :  int							=	batch_size
          UpperCamelCase    :  List[Any]							=	seq_length
          UpperCamelCase    :  int							=	is_training
          UpperCamelCase    :  str							=	use_input_mask
          UpperCamelCase    :  Optional[int]							=	use_token_type_ids
          UpperCamelCase    :  List[Any]							=	use_labels
          UpperCamelCase    :  str							=	vocab_size
          UpperCamelCase    :  List[str]							=	hidden_size
          UpperCamelCase    :  Any							=	num_hidden_layers
          UpperCamelCase    :  List[str]							=	num_attention_heads
          UpperCamelCase    :  str							=	intermediate_size
          UpperCamelCase    :  str							=	hidden_act
          UpperCamelCase    :  Any							=	hidden_dropout_prob
          UpperCamelCase    :  Optional[int]							=	attention_probs_dropout_prob
          UpperCamelCase    :  Optional[Any]							=	max_position_embeddings
          UpperCamelCase    :  Optional[Any]							=	type_vocab_size
          UpperCamelCase    :  List[Any]							=	type_sequence_label_size
          UpperCamelCase    :  Any							=	initializer_range
          UpperCamelCase    :  Optional[int]							=	num_labels
          UpperCamelCase    :  Union[str, Any]							=	num_choices
          UpperCamelCase    :  Dict							=	scope
          UpperCamelCase    :  Tuple							=	range_bbox
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Dict							=	ids_tensor([self.batch_size, self.seq_length],			self.vocab_size       )

          # convert bbox to numpy since TF does not support item assignment
          UpperCamelCase    :  List[str]							=	ids_tensor([self.batch_size, self.seq_length, 4],			self.range_bbox       ).numpy()
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]       ):
             for j in range(bbox.shape[1]       ):
                if bbox[i, j, 3] < bbox[i, j, 1]:
                   UpperCamelCase    :  int							=	bbox[i, j, 3]
                   UpperCamelCase    :  Optional[int]							=	bbox[i, j, 1]
                   UpperCamelCase    :  str							=	t
                if bbox[i, j, 2] < bbox[i, j, 0]:
                   UpperCamelCase    :  List[Any]							=	bbox[i, j, 2]
                   UpperCamelCase    :  Optional[int]							=	bbox[i, j, 0]
                   UpperCamelCase    :  Optional[int]							=	t
          UpperCamelCase    :  Union[str, Any]							=	tf.convert_to_tensor(UpperCamelCase__       )

          UpperCamelCase    :  int							=	None
          if self.use_input_mask:
             UpperCamelCase    :  Union[str, Any]							=	random_attention_mask([self.batch_size, self.seq_length]       )

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

          UpperCamelCase    :  Tuple							=	None
          UpperCamelCase    :  List[str]							=	None
          UpperCamelCase    :  Optional[int]							=	None
          if self.use_labels:
             UpperCamelCase    :  Any							=	ids_tensor([self.batch_size],			self.type_sequence_label_size       )
             UpperCamelCase    :  Union[str, Any]							=	ids_tensor([self.batch_size, self.seq_length],			self.num_labels       )
             UpperCamelCase    :  str							=	ids_tensor([self.batch_size],			self.num_choices       )

          UpperCamelCase    :  str							=	LayoutLMConfig(
              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,			initializer_range=self.initializer_range,			)

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		List[Any]:
          UpperCamelCase    :  Tuple							=	TFLayoutLMModel(config=UpperCamelCase__       )

          UpperCamelCase    :  Optional[Any]							=	model(UpperCamelCase__,			UpperCamelCase__,			attention_mask=UpperCamelCase__,			token_type_ids=UpperCamelCase__       )
          UpperCamelCase    :  List[Any]							=	model(UpperCamelCase__,			UpperCamelCase__,			token_type_ids=UpperCamelCase__       )
          UpperCamelCase    :  Any							=	model(UpperCamelCase__,			UpperCamelCase__       )
          self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)       )
          self.parent.assertEqual(result.pooler_output.shape,			(self.batch_size, self.hidden_size)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		str:
          UpperCamelCase    :  Optional[Any]							=	TFLayoutLMForMaskedLM(config=UpperCamelCase__       )

          UpperCamelCase    :  Optional[Any]							=	model(UpperCamelCase__,			UpperCamelCase__,			attention_mask=UpperCamelCase__,			token_type_ids=UpperCamelCase__,			labels=UpperCamelCase__       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.vocab_size)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		int:
          UpperCamelCase    :  Union[str, Any]							=	self.num_labels
          UpperCamelCase    :  Dict							=	TFLayoutLMForSequenceClassification(config=UpperCamelCase__       )

          UpperCamelCase    :  Optional[Any]							=	model(UpperCamelCase__,			UpperCamelCase__,			attention_mask=UpperCamelCase__,			token_type_ids=UpperCamelCase__       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          UpperCamelCase    :  Any							=	self.num_labels
          UpperCamelCase    :  List[Any]							=	TFLayoutLMForTokenClassification(config=UpperCamelCase__       )

          UpperCamelCase    :  Optional[Any]							=	model(UpperCamelCase__,			UpperCamelCase__,			attention_mask=UpperCamelCase__,			token_type_ids=UpperCamelCase__,			labels=UpperCamelCase__       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.num_labels)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		List[Any]:
          UpperCamelCase    :  str							=	TFLayoutLMForQuestionAnswering(config=UpperCamelCase__       )

          UpperCamelCase    :  Dict							=	model(UpperCamelCase__,			UpperCamelCase__,			attention_mask=UpperCamelCase__,			token_type_ids=UpperCamelCase__       )
          self.parent.assertEqual(result.start_logits.shape,			(self.batch_size, self.seq_length)       )
          self.parent.assertEqual(result.end_logits.shape,			(self.batch_size, self.seq_length)       )






       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Union[str, Any]							=	self.prepare_config_and_inputs()
          (
              UpperCamelCase
          )    :  Any							=	config_and_inputs
          UpperCamelCase    :  List[Any]							=	{
              "input_ids": input_ids,
              "bbox": bbox,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict






@require_tf
class 				lowerCAmelCase_		(  snake_case_       ,    snake_case_       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Any        =  (
           (
               TFLayoutLMModel,
               TFLayoutLMForMaskedLM,
               TFLayoutLMForTokenClassification,
               TFLayoutLMForSequenceClassification,
               TFLayoutLMForQuestionAnswering,
           )
           if is_tf_available()
           else ()
       )
       UpperCAmelCase__				:					List[str]        =  (
           {
               '''feature-extraction''': TFLayoutLMModel,
               '''fill-mask''': TFLayoutLMForMaskedLM,
               '''text-classification''': TFLayoutLMForSequenceClassification,
               '''token-classification''': TFLayoutLMForTokenClassification,
               '''zero-shot''': TFLayoutLMForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       UpperCAmelCase__				:					Any        =  False
       UpperCAmelCase__				:					List[Any]        =  True
       UpperCAmelCase__				:					Optional[Any]        =  10
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  List[Any]							=	TFLayoutLMModelTester(self       )
          UpperCamelCase    :  Optional[Any]							=	ConfigTester(self,			config_class=UpperCamelCase__,			hidden_size=37       )
       def 					snake_case_       (			self       )     ->		Any:
          self.config_tester.run_common_tests()
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  List[str]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCamelCase__       )
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  Dict							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__       )
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Tuple							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__       )
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  Any							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__       )
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Dict							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__       )
       @slow
       def 					snake_case_       (			self       )     ->		Optional[Any]:




          for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase    :  List[Any]							=	TFLayoutLMModel.from_pretrained(UpperCamelCase__       )
             self.assertIsNotNone(UpperCamelCase__       )






       @unittest.skip('Onnx compliancy broke with TF 2.10'       )
       def 					snake_case_       (			self       )     ->		List[str]:
          pass






def 		UpperCamelCase	(   )	->     List[str]:
   UpperCamelCase    :  Optional[Any]							=	tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]	)  # noqa: E231
   UpperCamelCase    :  Dict							=	tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]	)  # noqa: E231
   UpperCamelCase    :  Union[str, Any]							=	tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]	)  # noqa: E231
   UpperCamelCase    :  int							=	tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]	)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   UpperCamelCase    :  Optional[Any]							=	tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]	)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels



@require_tf
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Tuple							=	TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'       )

          UpperCamelCase    :  str							=	prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase    :  List[str]							=	model(input_ids=UpperCamelCase__,			bbox=UpperCamelCase__,			attention_mask=UpperCamelCase__,			token_type_ids=UpperCamelCase__       )

          # test the sequence output on [0, :3, :3]
          UpperCamelCase    :  Tuple							=	tf.convert_to_tensor(
              [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]],			)

          self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3],			UpperCamelCase__,			atol=1e-3       )       )

          # test the pooled output on [1, :3]
          UpperCamelCase    :  int							=	tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]       )

          self.assertTrue(np.allclose(outputs.pooler_output[1, :3],			UpperCamelCase__,			atol=1e-3       )       )
       @slow
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  List[Any]							=	TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased',			num_labels=2       )

          UpperCamelCase    :  str							=	prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase    :  Tuple							=	model(
              input_ids=UpperCamelCase__,			bbox=UpperCamelCase__,			attention_mask=UpperCamelCase__,			token_type_ids=UpperCamelCase__,			labels=tf.convert_to_tensor([1, 1]       ),			)

          # test whether we get a loss as a scalar
          UpperCamelCase    :  str							=	outputs.loss
          UpperCamelCase    :  Optional[Any]							=	(2,)
          self.assertEqual(loss.shape,			UpperCamelCase__       )

          # test the shape of the logits
          UpperCamelCase    :  Union[str, Any]							=	outputs.logits
          UpperCamelCase    :  Dict							=	(2, 2)
          self.assertEqual(logits.shape,			UpperCamelCase__       )
       @slow
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  List[Any]							=	TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased',			num_labels=13       )

          UpperCamelCase    :  Optional[int]							=	prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase    :  Optional[Any]							=	model(
              input_ids=UpperCamelCase__,			bbox=UpperCamelCase__,			attention_mask=UpperCamelCase__,			token_type_ids=UpperCamelCase__,			labels=UpperCamelCase__       )

          # test the shape of the logits
          UpperCamelCase    :  Dict							=	outputs.logits
          UpperCamelCase    :  Any							=	tf.convert_to_tensor((2, 25, 13)       )
          self.assertEqual(logits.shape,			UpperCamelCase__       )






       @slow
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Tuple							=	TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'       )

          UpperCamelCase    :  int							=	prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase    :  str							=	model(input_ids=UpperCamelCase__,			bbox=UpperCamelCase__,			attention_mask=UpperCamelCase__,			token_type_ids=UpperCamelCase__       )

          # test the shape of the logits
          UpperCamelCase    :  Union[str, Any]							=	tf.convert_to_tensor((2, 25)       )
          self.assertEqual(outputs.start_logits.shape,			UpperCamelCase__       )
          self.assertEqual(outputs.end_logits.shape,			UpperCamelCase__       )







"""simple docstring"""



import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


__snake_case   			=    """hf-internal-testing/tiny-random-bert"""
__snake_case   			=    os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
__snake_case   			=    """9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""







class _lowerCAmelCase				(      unittest.TestCase	):







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      int      					=    cached_file(UpperCamelCase__						,	UpperCamelCase__						)
       # Should have downloaded the file in here
       self.assertTrue(os.path.isdir(UpperCamelCase__						)						)
       # Cache should contain at least those three subfolders:
       for subfolder in ["blobs", "refs", "snapshots"]:
           self.assertTrue(os.path.isdir(os.path.join(UpperCamelCase__						,	UpperCamelCase__						)						)						)
       with open(os.path.join(UpperCamelCase__						,	"refs"						,	"main"						)						) as f:
           snake_case    :      Dict      					=    f.read()
       self.assertEqual(UpperCamelCase__						,	os.path.join(UpperCamelCase__						,	"snapshots"						,	UpperCamelCase__						,	UpperCamelCase__						)						)
       self.assertTrue(os.path.isfile(UpperCamelCase__						)						)

       # File is cached at the same place the second time.
       snake_case    :      List[str]      					=    cached_file(UpperCamelCase__						,	UpperCamelCase__						)
       self.assertEqual(UpperCamelCase__						,	UpperCamelCase__						)

       # Using a specific revision to test the full commit hash.
       snake_case    :      Any      					=    cached_file(UpperCamelCase__						,	UpperCamelCase__						,	revision="9b8c223"						)
       self.assertEqual(UpperCamelCase__						,	os.path.join(UpperCamelCase__						,	"snapshots"						,	UpperCamelCase__						,	UpperCamelCase__						)						)







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       with self.assertRaisesRegex(UpperCamelCase__						,	"is not a valid model identifier"						):
           snake_case    :      Optional[Any]      					=    cached_file("tiny-random-bert"						,	UpperCamelCase__						)

       with self.assertRaisesRegex(UpperCamelCase__						,	"is not a valid git identifier"						):
           snake_case    :      Optional[Any]      					=    cached_file(UpperCamelCase__						,	UpperCamelCase__						,	revision="aaaa"						)

       with self.assertRaisesRegex(UpperCamelCase__						,	"does not appear to have a file named"						):
           snake_case    :      List[Any]      					=    cached_file(UpperCamelCase__						,	"conf"						)







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       with self.assertRaisesRegex(UpperCamelCase__						,	"does not appear to have a file named"						):
           snake_case    :      Tuple      					=    cached_file(UpperCamelCase__						,	"conf"						)

       with open(os.path.join(UpperCamelCase__						,	"refs"						,	"main"						)						) as f:
           snake_case    :      Any      					=    f.read()
       self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__						,	".no_exist"						,	UpperCamelCase__						,	"conf"						)						)						)

       snake_case    :      Optional[Any]      					=    cached_file(UpperCamelCase__						,	"conf"						,	_raise_exceptions_for_missing_entries=UpperCamelCase__						)
       self.assertIsNone(UpperCamelCase__						)

       snake_case    :      Any      					=    cached_file(UpperCamelCase__						,	"conf"						,	local_files_only=UpperCamelCase__						,	_raise_exceptions_for_missing_entries=UpperCamelCase__						)
       self.assertIsNone(UpperCamelCase__						)

       snake_case    :      Any      					=    mock.Mock()
       snake_case    :      List[Any]      					=    500
       snake_case    :      int      					=    {}
       snake_case    :      Optional[int]      					=    HTTPError
       snake_case    :      Tuple      					=    {}

       # Under the mock environment we get a 500 error when trying to reach the tokenizer.
       with mock.patch("requests.Session.request"						,	return_value=UpperCamelCase__						) as mock_head:
           snake_case    :      Tuple      					=    cached_file(UpperCamelCase__						,	"conf"						,	_raise_exceptions_for_connection_errors=UpperCamelCase__						)
           self.assertIsNone(UpperCamelCase__						)
           # This check we did call the fake head request
           mock_head.assert_called()







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only"						,	UpperCamelCase__						)						)
       self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only"						,	UpperCamelCase__						)						)
       self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only"						,	UpperCamelCase__						)						)







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       self.assertIsNone(get_file_from_repo("bert-base-cased"						,	"ahah.txt"						)						)

       # The function raises if the repository does not exist.
       with self.assertRaisesRegex(UpperCamelCase__						,	"is not a valid model identifier"						):
           get_file_from_repo("bert-base-case"						,	UpperCamelCase__						)

       # The function raises if the revision does not exist.
       with self.assertRaisesRegex(UpperCamelCase__						,	"is not a valid git identifier"						):
           get_file_from_repo("bert-base-cased"						,	UpperCamelCase__						,	revision="ahaha"						)

       snake_case    :      int      					=    get_file_from_repo("bert-base-cased"						,	UpperCamelCase__						)
       # The name is the cached name which is not very easy to test, so instead we load the content.
       snake_case    :      str      					=    json.loads(open(UpperCamelCase__						,	"r"						).read()						)
       self.assertEqual(config["hidden_size"]						,	768						)







   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case    :      int      					=    Path(UpperCamelCase__						) / "a.txt"
           filename.touch()
           self.assertEqual(get_file_from_repo(UpperCamelCase__						,	"a.txt"						)						,	str(UpperCamelCase__						)						)

           self.assertIsNone(get_file_from_repo(UpperCamelCase__						,	"b.txt"						)						)





