

import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import MaskFormerSwinBackbone
	from transformers.models.maskformer import MaskFormerSwinModel

class 					a__						:
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase=13      ,      lowercase=32      ,      lowercase=2      ,      lowercase=3      ,      lowercase=16      ,      lowercase=[1, 2, 1]      ,      lowercase=[2, 2, 4]      ,      lowercase=2      ,      lowercase=2.0      ,      lowercase=True      ,      lowercase=0.0      ,      lowercase=0.0      ,      lowercase=0.1      ,      lowercase="gelu"      ,      lowercase=False      ,      lowercase=True      ,      lowercase=0.02      ,      lowercase=1e-5      ,      lowercase=True      ,      lowercase=None      ,      lowercase=True      ,      lowercase=10      ,      lowercase=8      ,      lowercase=["stage1", "stage2", "stage3"]      ,      lowercase=[1, 2, 3]      ,      )  ->						Tuple:
										'''simple docstring'''






										A__        =			parent
										A__        =			batch_size
										A__        =			image_size
										A__        =			patch_size
										A__        =			num_channels
										A__        =			embed_dim
										A__        =			depths
										A__        =			num_heads
										A__        =			window_size
										A__        =			mlp_ratio
										A__        =			qkv_bias
										A__        =			hidden_dropout_prob
										A__        =			attention_probs_dropout_prob
										A__        =			drop_path_rate
										A__        =			hidden_act
										A__        =			use_absolute_embeddings
										A__        =			patch_norm
										A__        =			layer_norm_eps
										A__        =			initializer_range
										A__        =			is_training
										A__        =			scope
										A__        =			use_labels
										A__        =			type_sequence_label_size
										A__        =			encoder_stride
										A__        =			out_features
										A__        =			out_indices


			def      UpperCamelCase		(    self       )  ->						Tuple:
										'''simple docstring'''






										A__        =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

										A__        =			None
										if self.use_labels:
																	A__        =			ids_tensor([self.batch_size]      ,      self.type_sequence_label_size       )

										A__        =			self.get_config()

										return config, pixel_values, labels


			def      UpperCamelCase		(    self       )  ->						Dict:
										'''simple docstring'''






										return MaskFormerSwinConfig(
										    image_size=self.image_size      ,      patch_size=self.patch_size      ,      num_channels=self.num_channels      ,      embed_dim=self.embed_dim      ,      depths=self.depths      ,      num_heads=self.num_heads      ,      window_size=self.window_size      ,      mlp_ratio=self.mlp_ratio      ,      qkv_bias=self.qkv_bias      ,      hidden_dropout_prob=self.hidden_dropout_prob      ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,      drop_path_rate=self.drop_path_rate      ,      hidden_act=self.hidden_act      ,      use_absolute_embeddings=self.use_absolute_embeddings      ,      path_norm=self.patch_norm      ,      layer_norm_eps=self.layer_norm_eps      ,      initializer_range=self.initializer_range      ,      encoder_stride=self.encoder_stride      ,      out_features=self.out_features      ,      out_indices=self.out_indices      ,      )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						str:
										'''simple docstring'''






										A__        =			MaskFormerSwinModel(config=__A       )
										model.to(__A       )
										model.eval()
										A__        =			model(__A       )

										A__        =			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
										A__        =			int(config.embed_dim * 2 ** (len(config.depths       ) - 1)       )

										self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, expected_seq_len, expected_dim)       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						List[Any]:
										'''simple docstring'''






										A__        =			MaskFormerSwinBackbone(config=__A       )
										model.to(__A       )
										model.eval()
										A__        =			model(__A       )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps       )      ,      len(config.out_features       )       )
										self.parent.assertListEqual(list(result.feature_maps[0].shape       )      ,      [13, 16, 16, 16]       )

										# verify channels
										self.parent.assertEqual(len(model.channels       )      ,      len(config.out_features       )       )
										self.parent.assertListEqual(model.channels      ,      [16, 32, 64]       )

										# verify ValueError
										with self.parent.assertRaises(__A       ):
																	A__        =			["stem"]
																	A__        =			MaskFormerSwinBackbone(config=__A       )


			def      UpperCamelCase		(    self       )  ->						Dict:
										'''simple docstring'''






										A__        =			self.prepare_config_and_inputs()
										A__    ,				A__    ,				A__        =			config_and_inputs
										A__        =			{"pixel_values": pixel_values}
										return config, inputs_dict

@require_torch
class 					a__						(					snake_case							,	snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       (
			    (
			        MaskFormerSwinModel,
			        MaskFormerSwinBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			__lowerCamelCase        =       {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
			__lowerCamelCase        =       False
			__lowerCamelCase        =       False
			__lowerCamelCase        =       False
			__lowerCamelCase        =       False
			__lowerCamelCase        =       False


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
										'''simple docstring'''






										A__        =			MaskFormerSwinModelTester(self       )
										A__        =			ConfigTester(self      ,      config_class=__A      ,      embed_dim=37       )


			@require_torch_multi_gpu
			@unittest.skip(
			    reason=(
			        "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with"
			        " `nn.DataParallel`"
			    )       )
			def      UpperCamelCase		(    self       )  ->						List[Any]:
										'''simple docstring'''






										pass


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
										'''simple docstring'''






										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
										'''simple docstring'''






										return


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
										'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						Any:
										'''simple docstring'''






										A__        =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*__A       )


			@unittest.skip("Swin does not use inputs_embeds"       )
			def      UpperCamelCase		(    self       )  ->						Any:
										'''simple docstring'''






										pass


			@unittest.skip("Swin does not support feedforward chunking"       )
			def      UpperCamelCase		(    self       )  ->						List[str]:
										'''simple docstring'''






										pass


			def      UpperCamelCase		(    self       )  ->						Tuple:
										'''simple docstring'''






										A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	A__        =			model_class(__A       )
																	self.assertIsInstance(model.get_input_embeddings()      ,      (nn.Module)       )
																	A__        =			model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(__A      ,      nn.Linear       )       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
										'''simple docstring'''






										A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	A__        =			model_class(__A       )
																	A__        =			inspect.signature(model.forward       )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	A__        =			[*signature.parameters.keys()]

																	A__        =			["pixel_values"]
																	self.assertListEqual(arg_names[:1]      ,      __A       )


			@unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn\'t support output_attentions"       )
			def      UpperCamelCase		(    self       )  ->						List[Any]:
										'''simple docstring'''






										pass


			@unittest.skip(reason="MaskFormerSwin is only used as an internal backbone"       )
			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
										'''simple docstring'''






										pass


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Any:
										'''simple docstring'''






										A__        =			model_class(__A       )
										model.to(__A       )
										model.eval()

										with torch.no_grad():
																	A__        =			model(**self._prepare_for_class(__A      ,      __A       )       )

										A__        =			outputs.hidden_states

										A__        =			getattr(
										    self.model_tester      ,      "expected_num_hidden_layers"      ,      len(self.model_tester.depths       ) + 1       )
										self.assertEqual(len(__A       )      ,      __A       )

										# Swin has a different seq_length
										A__        =			(
										    config.patch_size
										    if isinstance(config.patch_size      ,      collections.abc.Iterable       )
										    else (config.patch_size, config.patch_size)
										)

										A__        =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]       )      ,      [num_patches, self.model_tester.embed_dim]      ,      )


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
										'''simple docstring'''






										A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

										A__        =			(
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size      ,      collections.abc.Iterable       )
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)

										for model_class in self.all_model_classes:
																	A__        =			True
																	self.check_hidden_states_output(__A      ,      __A      ,      __A      ,      __A       )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	A__        =			True

																	self.check_hidden_states_output(__A      ,      __A      ,      __A      ,      __A       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
										'''simple docstring'''






										A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()
										A__        =			3

										A__        =			(
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size      ,      collections.abc.Iterable       )
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)
										A__        =			(
										    config.patch_size
										    if isinstance(config.patch_size      ,      collections.abc.Iterable       )
										    else (config.patch_size, config.patch_size)
										)

										A__        =			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
										A__        =			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

										for model_class in self.all_model_classes:
																	A__        =			True
																	self.check_hidden_states_output(__A      ,      __A      ,      __A      ,      (padded_height, padded_width)       )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	A__        =			True
																	self.check_hidden_states_output(__A      ,      __A      ,      __A      ,      (padded_height, padded_width)       )


			@unittest.skip(reason="MaskFormerSwin doesn\'t have pretrained checkpoints"       )
			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
										'''simple docstring'''






										pass


			@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"       )
			def      UpperCamelCase		(    self       )  ->						Dict:
										'''simple docstring'''






										pass


			@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"       )
			def      UpperCamelCase		(    self       )  ->						str:
										'''simple docstring'''






										pass


			def      UpperCamelCase		(    self       )  ->						List[Any]:
										'''simple docstring'''






										A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

										def set_nan_tensor_to_zero(lowercase       ):
																	A__        =			0
																	return t

										def check_equivalence(lowercase      ,      lowercase      ,      lowercase      ,      lowercase={}       ):
																	with torch.no_grad():
																								A__        =			model(**__A      ,      return_dict=__A      ,      **__A       )
																								A__        =			model(**__A      ,      return_dict=__A      ,      **__A       ).to_tuple()

																								def recursive_check(lowercase      ,      lowercase       ):
																															if isinstance(__A      ,      (List, Tuple)       ):
																																						for tuple_iterable_value, dict_iterable_value in zip(__A      ,      __A       ):
																																													recursive_check(__A      ,      __A       )
																															elif isinstance(__A      ,      __A       ):
																																						for tuple_iterable_value, dict_iterable_value in zip(
																																						    tuple_object.values()      ,      dict_object.values()       ):
																																													recursive_check(__A      ,      __A       )
																															elif tuple_object is None:
																																						return
																															else:
																																						self.assertTrue(
																																						    torch.allclose(
																																						        set_nan_tensor_to_zero(__A       )      ,      set_nan_tensor_to_zero(__A       )      ,      atol=1e-5       )      ,      msg=(
																																						        "Tuple and dict output are not equal. Difference:"
																																						        F' {torch.max(torch.abs(tuple_object - dict_object       )       )}. Tuple has `nan`:'
																																						        F' {torch.isnan(__A       ).any()} and `inf`: {torch.isinf(__A       )}. Dict has'
																																						        F' `nan`: {torch.isnan(__A       ).any()} and `inf`: {torch.isinf(__A       )}.'
																																						    )      ,      )

																								recursive_check(__A      ,      __A       )

										for model_class in self.all_model_classes:
																	A__        =			model_class(__A       )
																	model.to(__A       )
																	model.eval()

																	A__        =			self._prepare_for_class(__A      ,      __A       )
																	A__        =			self._prepare_for_class(__A      ,      __A       )
																	check_equivalence(__A      ,      __A      ,      __A       )

																	A__        =			self._prepare_for_class(__A      ,      __A      ,      return_labels=__A       )
																	A__        =			self._prepare_for_class(__A      ,      __A      ,      return_labels=__A       )
																	check_equivalence(__A      ,      __A      ,      __A       )

																	A__        =			self._prepare_for_class(__A      ,      __A       )
																	A__        =			self._prepare_for_class(__A      ,      __A       )
																	check_equivalence(__A      ,      __A      ,      __A      ,      {"output_hidden_states": True}       )

																	A__        =			self._prepare_for_class(__A      ,      __A      ,      return_labels=__A       )
																	A__        =			self._prepare_for_class(__A      ,      __A      ,      return_labels=__A       )
																	check_equivalence(__A      ,      __A      ,      __A      ,      {"output_hidden_states": True}       )

@require_torch
class 					a__						(					unittest.TestCase							,	snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       (MaskFormerSwinBackbone,) if is_torch_available() else ()
			__lowerCamelCase        =       MaskFormerSwinConfig


			def      UpperCamelCase		(    self       )  ->						int:
										'''simple docstring'''






										A__        =			MaskFormerSwinModelTester(self       )


			def      UpperCamelCase		(    self       )  ->						Any:
										'''simple docstring'''






										A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()
										A__        =			inputs_dict["pixel_values"].shape[0]

										for backbone_class in self.all_model_classes:
																	A__        =			backbone_class(__A       )
																	backbone.to(__A       )
																	backbone.eval()

																	A__        =			backbone(**__A       )

																	# Test default outputs and verify feature maps
																	self.assertIsInstance(outputs.feature_maps      ,      __A       )
																	self.assertTrue(len(outputs.feature_maps       ) == len(backbone.channels       )       )
																	for feature_map, n_channels in zip(outputs.feature_maps      ,      backbone.channels       ):
																								self.assertTrue(feature_map.shape[:2]      ,      (batch_size, n_channels)       )
																	self.assertIsNone(outputs.hidden_states       )
																	self.assertIsNone(outputs.attentions       )

																	# Test output_hidden_states=True
																	A__        =			backbone(**__A      ,      output_hidden_states=__A       )
																	self.assertIsNotNone(outputs.hidden_states       )
																	self.assertTrue(len(outputs.hidden_states       )      ,      len(backbone.stage_names       )       )
																	# We skip the stem layer
																	for hidden_states, n_channels in zip(outputs.hidden_states[1:]      ,      backbone.channels       ):
																								for hidden_state in hidden_states:
																															# Hidden states are in the format (batch_size, (height * width), n_channels)
																															A__    ,				A__    ,				A__        =			hidden_state.shape
																															self.assertTrue((h_batch_size, h_n_channels)      ,      (batch_size, n_channels)       )

            # Test output_attentions=True
																	if self.has_attentions:
																								A__        =			backbone(**__A      ,      output_attentions=__A       )
																								self.assertIsNotNone(outputs.attentions       )




from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
	from transformers import (
	    TFAutoModel,
	    TFAutoModelForCausalLM,
	    TFAutoModelForMaskedLM,
	    TFAutoModelForPreTraining,
	    TFAutoModelForQuestionAnswering,
	    TFAutoModelForSeqaSeqLM,
	    TFAutoModelForSequenceClassification,
	    TFAutoModelForTableQuestionAnswering,
	    TFAutoModelForTokenClassification,
	    TFAutoModelWithLMHead,
	    TFBertForMaskedLM,
	    TFBertForPreTraining,
	    TFBertForQuestionAnswering,
	    TFBertForSequenceClassification,
	    TFBertModel,
	    TFFunnelBaseModel,
	    TFFunnelModel,
	    TFGPTaLMHeadModel,
	    TFRobertaForMaskedLM,
	    TFTaForConditionalGeneration,
	    TFTapasForQuestionAnswering,
	)
	from transformers.models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_MASKED_LM_MAPPING,
	    TF_MODEL_FOR_PRETRAINING_MAPPING,
	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    TF_MODEL_MAPPING,
	)
	from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST

class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       'new-model'


if is_tf_available():

	class 					a__						(					snake_case			):
				"""simple docstring"""







				__lowerCamelCase        =       NewModelConfig

@require_tf
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			@slow
			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			"bert-base-cased"
							A__        =			AutoConfig.from_pretrained(lowercase       )
							self.assertIsNotNone(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )

							A__        =			TFAutoModel.from_pretrained(lowercase       )
							self.assertIsNotNone(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			"bert-base-cased"
							A__        =			AutoConfig.from_pretrained(lowercase       )
							self.assertIsNotNone(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )

							A__        =			TFAutoModelForPreTraining.from_pretrained(lowercase       )
							self.assertIsNotNone(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForCausalLM.from_pretrained(lowercase       )
											A__    ,				A__        =			TFAutoModelForCausalLM.from_pretrained(lowercase      ,      output_loading_info=lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelWithLMHead.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForMaskedLM.from_pretrained(lowercase       )
											A__    ,				A__        =			TFAutoModelForMaskedLM.from_pretrained(lowercase      ,      output_loading_info=lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForSeqaSeqLM.from_pretrained(lowercase       )
											A__    ,				A__        =			TFAutoModelForSeqaSeqLM.from_pretrained(lowercase      ,      output_loading_info=lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							for model_name in ["bert-base-uncased"]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForSequenceClassification.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							for model_name in ["bert-base-uncased"]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForQuestionAnswering.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			@require_tensorflow_probability
			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase       )
											A__    ,				A__        =			TFAutoModelForTableQuestionAnswering.from_pretrained(
											    lowercase      ,      output_loading_info=lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			TFAutoModelWithLMHead.from_pretrained(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )
							self.assertEqual(model.num_parameters()      ,      14410       )
							self.assertEqual(model.num_parameters(only_trainable=lowercase       )      ,      14410       )


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			TFAutoModelWithLMHead.from_pretrained(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )
							self.assertEqual(model.num_parameters()      ,      14410       )
							self.assertEqual(model.num_parameters(only_trainable=lowercase       )      ,      14410       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			TFAutoModel.from_pretrained("sgugger/funnel-random-tiny"       )
							self.assertIsInstance(lowercase      ,      lowercase       )

							A__        =			copy.deepcopy(model.config       )
							A__        =			["FunnelBaseModel"]
							A__        =			TFAutoModel.from_config(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )

							with tempfile.TemporaryDirectory() as tmp_dir:
											model.save_pretrained(lowercase       )
											A__        =			TFAutoModel.from_pretrained(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							try:
											AutoConfig.register("new-model"      ,      lowercase       )

											A__        =			[
											    TFAutoModel,
											    TFAutoModelForCausalLM,
											    TFAutoModelForMaskedLM,
											    TFAutoModelForPreTraining,
											    TFAutoModelForQuestionAnswering,
											    TFAutoModelForSequenceClassification,
											    TFAutoModelForTokenClassification,
											]

											for auto_class in auto_classes:
															with self.subTest(auto_class.__name__       ):
																			# Wrong config class will raise an error
																			with self.assertRaises(lowercase       ):
																							auto_class.register(lowercase      ,      lowercase       )
																			auto_class.register(lowercase      ,      lowercase       )
																			# Trying to register something existing in the Transformers library will raise an error
																			with self.assertRaises(lowercase       ):
																							auto_class.register(lowercase      ,      lowercase       )

																			# Now that the config is registered, it can be used as any other config with the auto-API
																			A__        =			BertModelTester(self       ).get_config()
																			A__        =			NewModelConfig(**tiny_config.to_dict()       )
																			A__        =			auto_class.from_config(lowercase       )
																			self.assertIsInstance(lowercase      ,      lowercase       )

																			with tempfile.TemporaryDirectory() as tmp_dir:
																							model.save_pretrained(lowercase       )
																							A__        =			auto_class.from_pretrained(lowercase       )
																							self.assertIsInstance(lowercase      ,      lowercase       )

							finally:
											if "new-model" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["new-model"]
											for mapping in (
											    TF_MODEL_MAPPING,
											    TF_MODEL_FOR_PRETRAINING_MAPPING,
											    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
											    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
											    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
											    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
											    TF_MODEL_FOR_MASKED_LM_MAPPING,
											):
															if NewModelConfig in mapping._extra_content:
																			del mapping._extra_content[NewModelConfig]


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							with self.assertRaisesRegex(
							    lowercase      ,      "bert-base is not a local folder and is not a valid model identifier"       ):
											A__        =			TFAutoModel.from_pretrained("bert-base"       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							with self.assertRaisesRegex(
							    lowercase      ,      R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"       ):
											A__        =			TFAutoModel.from_pretrained(lowercase      ,      revision="aaaaaa"       )


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							with self.assertRaisesRegex(
							    lowercase      ,      "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"      ,      ):
											A__        =			TFAutoModel.from_pretrained("hf-internal-testing/config-no-model"       )


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							with self.assertRaisesRegex(lowercase      ,      "Use `from_pt=True` to load this model"       ):
											A__        =			TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"       )


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"       )
							with RequestCounter() as counter:
											A__        =			TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"       )
											self.assertEqual(counter.get_request_count      ,      0       )
											self.assertEqual(counter.head_request_count      ,      1       )
											self.assertEqual(counter.other_request_count      ,      0       )

							# With a sharded checkpoint
							A__        =			TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"       )
							with RequestCounter() as counter:
											A__        =			TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"       )
											self.assertEqual(counter.get_request_count      ,      0       )
											self.assertEqual(counter.head_request_count      ,      1       )
											self.assertEqual(counter.other_request_count      ,      0       )

