


'''simple docstring'''



import unittest

from transformers import XLMConfig, is_torch_available
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 (
	    XLMForMultipleChoice,
	    XLMForQuestionAnswering,
	    XLMForQuestionAnsweringSimple,
	    XLMForSequenceClassification,
	    XLMForTokenClassification,
	    XLMModel,
	    XLMWithLMHeadModel,
	)
	from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST





class 							__UpperCamelCase				:




	def __init__(   self						,				__a						,				__a=13						,				__a=7						,				__a=True						,				__a=True						,				__a=True						,				__a=True						,				__a=True						,				__a=False						,				__a=False						,				__a=False						,				__a=2						,				__a=99						,				__a=0						,				__a=32						,				__a=5						,				__a=4						,				__a=0.1						,				__a=0.1						,				__a=512						,				__a=2						,				__a=0.02						,				__a=2						,				__a=4						,				__a="last"						,				__a=True						,				__a=None						,				__a=0						,				):

						'''simple docstring'''





						__a			:       List[str]					    =  parent
						__a			:       int					    =  batch_size
						__a			:       str					    =  seq_length
						__a			:       Optional[int]					    =  is_training
						__a			:       Any					    =  use_input_lengths
						__a			:       Optional[int]					    =  use_token_type_ids
						__a			:       int					    =  use_labels
						__a			:       Dict					    =  gelu_activation
						__a			:       int					    =  sinusoidal_embeddings
						__a			:       str					    =  causal
						__a			:       Union[str, Any]					    =  asm
						__a			:       Optional[Any]					    =  n_langs
						__a			:       Union[str, Any]					    =  vocab_size
						__a			:       List[str]					    =  n_special
						__a			:       Tuple					    =  hidden_size
						__a			:       Any					    =  num_hidden_layers
						__a			:       Dict					    =  num_attention_heads
						__a			:       Tuple					    =  hidden_dropout_prob
						__a			:       Dict					    =  attention_probs_dropout_prob
						__a			:       Tuple					    =  max_position_embeddings
						__a			:       Optional[Any]					    =  type_sequence_label_size
						__a			:       List[Any]					    =  initializer_range
						__a			:       Tuple					    =  num_labels
						__a			:       List[str]					    =  num_choices
						__a			:       str					    =  summary_type
						__a			:       int					    =  use_proj
						__a			:       Dict					    =  scope
						__a			:       Optional[int]					    =  bos_token_id




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size							)
						__a			:       List[Any]					    =  random_attention_mask([self.batch_size, self.seq_length]							)

						__a			:       Tuple					    =  None
						if self.use_input_lengths:
											__a			:       List[str]					    =  (
											    ids_tensor([self.batch_size]						,				vocab_size=2							) + self.seq_length - 2
											)  # small variation of seq_length

						__a			:       Optional[Any]					    =  None
						if self.use_token_type_ids:
											__a			:       Optional[Any]					    =  ids_tensor([self.batch_size, self.seq_length]						,				self.n_langs							)

						__a			:       Optional[Any]					    =  None
						__a			:       Optional[int]					    =  None
						__a			:       List[Any]					    =  None
						if self.use_labels:
											__a			:       Tuple					    =  ids_tensor([self.batch_size]						,				self.type_sequence_label_size							)
											__a			:       List[Any]					    =  ids_tensor([self.batch_size, self.seq_length]						,				self.num_labels							)
											__a			:       List[Any]					    =  ids_tensor([self.batch_size]						,				2							).float()
											__a			:       Any					    =  ids_tensor([self.batch_size]						,				self.num_choices							)

						__a			:       List[str]					    =  self.get_config()

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_lengths,
						    sequence_labels,
						    token_labels,
						    is_impossible_labels,
						    choice_labels,
						    input_mask,
						)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return XLMConfig(
						    vocab_size=self.vocab_size						,				n_special=self.n_special						,				emb_dim=self.hidden_size						,				n_layers=self.num_hidden_layers						,				n_heads=self.num_attention_heads						,				dropout=self.hidden_dropout_prob						,				attention_dropout=self.attention_probs_dropout_prob						,				gelu_activation=self.gelu_activation						,				sinusoidal_embeddings=self.sinusoidal_embeddings						,				asm=self.asm						,				causal=self.causal						,				n_langs=self.n_langs						,				max_position_embeddings=self.max_position_embeddings						,				initializer_range=self.initializer_range						,				summary_type=self.summary_type						,				use_proj=self.use_proj						,				num_labels=self.num_labels						,				bos_token_id=self.bos_token_id						,				)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       Any					    =  XLMModel(config=__a							)
						model.to(__a							)
						model.eval()
						__a			:       Optional[Any]					    =  model(__a						,				lengths=__a						,				langs=__a							)
						__a			:       Union[str, Any]					    =  model(__a						,				langs=__a							)
						__a			:       Optional[int]					    =  model(__a							)
						self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  XLMWithLMHeadModel(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Optional[Any]					    =  model(__a						,				token_type_ids=__a						,				labels=__a							)
						self.parent.assertEqual(result.loss.shape						,				()							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.vocab_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       List[str]					    =  XLMForQuestionAnsweringSimple(__a							)
						model.to(__a							)
						model.eval()

						__a			:       List[str]					    =  model(__a							)

						__a			:       Optional[int]					    =  model(__a						,				start_positions=__a						,				end_positions=__a							)
						__a			:       Optional[Any]					    =  outputs
						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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       Any					    =  XLMForQuestionAnswering(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Dict					    =  model(__a							)

						__a			:       Optional[Any]					    =  model(
						    __a						,				start_positions=__a						,				end_positions=__a						,				cls_index=__a						,				is_impossible=__a						,				p_mask=__a						,				)

						__a			:       List[Any]					    =  model(
						    __a						,				start_positions=__a						,				end_positions=__a						,				cls_index=__a						,				is_impossible=__a						,				)

						((__a)    ,				)			:       Union[str, Any]					    =  result_with_labels.to_tuple()

						__a			:       Union[str, Any]					    =  model(__a						,				start_positions=__a						,				end_positions=__a							)

						((__a)    ,				)			:       Dict					    =  result_with_labels.to_tuple()

						self.parent.assertEqual(result_with_labels.loss.shape						,				()							)
						self.parent.assertEqual(result.start_top_log_probs.shape						,				(self.batch_size, model.config.start_n_top)							)
						self.parent.assertEqual(result.start_top_index.shape						,				(self.batch_size, model.config.start_n_top)							)
						self.parent.assertEqual(
						    result.end_top_log_probs.shape						,				(self.batch_size, model.config.start_n_top * model.config.end_n_top)							)
						self.parent.assertEqual(
						    result.end_top_index.shape						,				(self.batch_size, model.config.start_n_top * model.config.end_n_top)							)
						self.parent.assertEqual(result.cls_logits.shape						,				(self.batch_size,)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       List[Any]					    =  XLMForSequenceClassification(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Optional[int]					    =  model(__a							)
						__a			:       Union[str, Any]					    =  model(__a						,				labels=__a							)
						self.parent.assertEqual(result.loss.shape						,				()							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.type_sequence_label_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       List[Any]					    =  self.num_labels
						__a			:       Any					    =  XLMForTokenClassification(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Optional[int]					    =  model(__a						,				attention_mask=__a						,				labels=__a							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.num_labels)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       str					    =  self.num_choices
						__a			:       Union[str, Any]					    =  XLMForMultipleChoice(config=__a							)
						model.to(__a							)
						model.eval()
						__a			:       int					    =  input_ids.unsqueeze(1							).expand(-1						,				self.num_choices						,				-1							).contiguous()
						__a			:       Any					    =  token_type_ids.unsqueeze(1							).expand(-1						,				self.num_choices						,				-1							).contiguous()
						__a			:       Optional[Any]					    =  input_mask.unsqueeze(1							).expand(-1						,				self.num_choices						,				-1							).contiguous()
						__a			:       str					    =  model(
						    __a						,				attention_mask=__a						,				token_type_ids=__a						,				labels=__a						,				)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_choices)							)





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  self.prepare_config_and_inputs()
						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       Optional[int]					    =  config_and_inputs
						__a			:       Tuple					    =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
						return config, inputs_dict





@require_torch
class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    (
	    (
	        XLMModel,
	        XLMWithLMHeadModel,
	        XLMForQuestionAnswering,
	        XLMForSequenceClassification,
	        XLMForQuestionAnsweringSimple,
	        XLMForTokenClassification,
	        XLMForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	A_				=    (
	    (XLMWithLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	A_				=    (
	    {
	        "feature-extraction": XLMModel,
	        "fill-mask": XLMWithLMHeadModel,
	        "question-answering": XLMForQuestionAnsweringSimple,
	        "text-classification": XLMForSequenceClassification,
	        "text-generation": XLMWithLMHeadModel,
	        "token-classification": XLMForTokenClassification,
	        "zero-shot": XLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						if (
						    pipeline_test_casse_name == "QAPipelineTests"
						    and tokenizer_name is not None
						    and not tokenizer_name.endswith('Fast'							)
						):
											# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
											# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
											# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
											return True

						return False




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=False							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  super()._prepare_for_class(__a						,				__a						,				return_labels=__a							)

						if return_labels:
											if model_class.__name__ == "XLMForQuestionAnswering":
																__a			:       List[Any]					    =  torch.zeros(
																    self.model_tester.batch_size						,				dtype=torch.long						,				device=__a							)
																__a			:       Any					    =  torch.zeros(
																    self.model_tester.batch_size						,				dtype=torch.long						,				device=__a							)

						return inputs_dict




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  XLMModelTester(self							)
						__a			:       Union[str, Any]					    =  ConfigTester(self						,				config_class=__a						,				emb_dim=37							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.config_tester.run_common_tests()




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_model(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_lm_head(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_simple_qa(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_qa(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_sequence_classif(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_token_classif(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_multiple_choice(*__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a=False						,				__a=1							):

						'''simple docstring'''





						self.assertIsInstance(__a						,				__a							)
						self.assertListEqual(
						    [isinstance(__a						,				__a							) for iter_attentions in attentions]						,				[True] * len(__a							)							)
						self.assertEqual(len(__a							)						,				(max_length - min_length) * num_beam_groups							)

						for idx, iter_attentions in enumerate(__a							):
											# adds PAD dummy token
											__a			:       Optional[Any]					    =  min_length + idx + 1
											__a			:       List[str]					    =  min_length + idx + 1

											__a			:       List[Any]					    =  (
											    batch_size * num_beam_groups,
											    config.num_attention_heads,
											    tgt_len,
											    src_len,
											)
											# check attn size
											self.assertListEqual(
											    [layer_attention.shape for layer_attention in iter_attentions]						,				[expected_shape] * len(__a							)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a=False						,				__a=1							):

						'''simple docstring'''





						self.assertIsInstance(__a						,				__a							)
						self.assertListEqual(
						    [isinstance(__a						,				__a							) for iter_hidden_states in hidden_states]						,				[True] * len(__a							)						,				)
						self.assertEqual(len(__a							)						,				(max_length - min_length) * num_beam_groups							)

						for idx, iter_hidden_states in enumerate(__a							):
											# adds PAD dummy token
											__a			:       List[str]					    =  min_length + idx + 1
											__a			:       Dict					    =  (batch_size * num_beam_groups, seq_len, config.hidden_size)
											# check hidden size
											self.assertListEqual(
											    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]						,				[expected_shape] * len(__a							)						,				)
						pass





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a			:       Optional[Any]					    =  XLMModel.from_pretrained(__a							)
											self.assertIsNotNone(__a							)





@require_torch
class 							__UpperCamelCase				(		unittest.TestCase					):




	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'							)
						model.to(__a							)
						__a			:       Union[str, Any]					    =  torch.tensor([[14, 447]]						,				dtype=torch.long						,				device=__a							)  # the president
						__a			:       int					    =  [
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						]  # the president the president the president the president the president the president the president the president the president the president
						# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
						__a			:       Optional[Any]					    =  model.generate(__a						,				do_sample=__a							)
						self.assertListEqual(output_ids[0].cpu().numpy().tolist()						,				__a							)







'''simple docstring'''



from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
	import tensorflow as tf

	from transformers import TFCvtForImageClassification, TFCvtModel
	from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  self.config_class(**self.inputs_dict							)
						self.parent.assertTrue(hasattr(__a						,				'embed_dim'							)							)
						self.parent.assertTrue(hasattr(__a						,				'num_heads'							)							)





class 							__UpperCamelCase				:




	def __init__(   self						,				__a						,				__a=13						,				__a=64						,				__a=3						,				__a=[16, 48, 96]						,				__a=[1, 3, 6]						,				__a=[1, 2, 10]						,				__a=[7, 3, 3]						,				__a=[4, 2, 2]						,				__a=[2, 1, 1]						,				__a=[2, 2, 2]						,				__a=[False, False, True]						,				__a=[0.0, 0.0, 0.0]						,				__a=0.02						,				__a=1E-1_2						,				__a=True						,				__a=True						,				__a=2						,				):

						'''simple docstring'''





						__a			:       str					    =  parent
						__a			:       List[Any]					    =  batch_size
						__a			:       Optional[int]					    =  image_size
						__a			:       List[str]					    =  patch_sizes
						__a			:       str					    =  patch_stride
						__a			:       Any					    =  patch_padding
						__a			:       Dict					    =  is_training
						__a			:       Union[str, Any]					    =  use_labels
						__a			:       Dict					    =  num_labels
						__a			:       List[Any]					    =  num_channels
						__a			:       Any					    =  embed_dim
						__a			:       int					    =  num_heads
						__a			:       Optional[int]					    =  stride_kv
						__a			:       Dict					    =  depth
						__a			:       List[str]					    =  cls_token
						__a			:       List[Any]					    =  attention_drop_rate
						__a			:       Tuple					    =  initializer_range
						__a			:       int					    =  layer_norm_eps




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						__a			:       Dict					    =  None
						if self.use_labels:
											# create a random int32 tensor of given shape
											__a			:       str					    =  ids_tensor([self.batch_size]						,				self.num_labels							)

						__a			:       str					    =  self.get_config()
						return config, pixel_values, labels




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return CvtConfig(
						    image_size=self.image_size						,				num_labels=self.num_labels						,				num_channels=self.num_channels						,				embed_dim=self.embed_dim						,				num_heads=self.num_heads						,				patch_sizes=self.patch_sizes						,				patch_padding=self.patch_padding						,				patch_stride=self.patch_stride						,				stride_kv=self.stride_kv						,				depth=self.depth						,				cls_token=self.cls_token						,				attention_drop_rate=self.attention_drop_rate						,				initializer_range=self.initializer_range						,				)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  TFCvtModel(config=__a							)
						__a			:       Dict					    =  model(__a						,				training=__a							)
						__a			:       Any					    =  (self.image_size, self.image_size)
						__a    ,				__a			:       Dict					    =  image_size[0], image_size[1]
						for i in range(len(self.depth							)							):
											__a			:       Tuple					    =  floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1							)
											__a			:       str					    =  floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1							)
						self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.embed_dim[-1], height, width)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       List[Any]					    =  self.num_labels
						__a			:       Optional[int]					    =  TFCvtForImageClassification(__a							)
						__a			:       Dict					    =  model(__a						,				labels=__a						,				training=__a							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels)							)





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  self.prepare_config_and_inputs()
						__a    ,				__a    ,				__a			:       Tuple					    =  config_and_inputs
						__a			:       str					    =  {'pixel_values': pixel_values}
						return config, inputs_dict





@require_tf
class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
	A_				=    (
	    {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
	    if is_tf_available()
	    else {}
	)
	A_				=    False
	A_				=    False
	A_				=    False
	A_				=    False
	A_				=    False




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  TFCvtModelTester(self							)
						__a			:       List[Any]					    =  TFCvtConfigTester(self						,				config_class=__a						,				has_text_modality=__a						,				hidden_size=37							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.config_tester.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()




	@unittest.skip(reason='Cvt does not output attentions'							)
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						pass




	@unittest.skip(reason='Cvt does not use inputs_embeds'							)
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						pass




	@unittest.skip(reason='Cvt does not support input and output embeddings'							)
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						pass




	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices('GPU'							)							) == 0						,				reason='TF does not support backprop for grouped convolutions on CPU.'						,				)
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().test_dataset_conversion()




	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices('GPU'							)							) == 0						,				reason='TF does not support backprop for grouped convolutions on CPU.'						,				)
	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().test_keras_fit()




	@unittest.skip(reason='Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'							)
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  tf.keras.mixed_precision.Policy('mixed_float16'							)
						tf.keras.mixed_precision.set_global_policy(__a							)
						super().test_keras_fit()
						tf.keras.mixed_precision.set_global_policy('float32'							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a    ,				__a			:       List[Any]					    =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__a			:       Dict					    =  model_class(__a							)
											__a			:       Optional[Any]					    =  inspect.signature(model.call							)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__a			:       Optional[Any]					    =  [*signature.parameters.keys()]

											__a			:       Optional[Any]					    =  ['pixel_values']
											self.assertListEqual(arg_names[:1]						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						def check_hidden_states_output(__a						,				__a						,				__a							):
											__a			:       List[str]					    =  model_class(__a							)

											__a			:       Union[str, Any]					    =  model(**self._prepare_for_class(__a						,				__a							)							)
											__a			:       Any					    =  outputs.hidden_states

											__a			:       Union[str, Any]					    =  len(self.model_tester.depth							)
											self.assertEqual(len(__a							)						,				__a							)

											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-3:]							)						,				[
											        self.model_tester.embed_dim[0],
											        self.model_tester.image_size // 4,
											        self.model_tester.image_size // 4,
											    ]						,				)

						__a    ,				__a			:       Optional[int]					    =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__a			:       List[str]					    =  True
											check_hidden_states_output(__a						,				__a						,				__a							)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											__a			:       Optional[Any]					    =  True

											check_hidden_states_output(__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__a							)





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a			:       Optional[Any]					    =  TFCvtModel.from_pretrained(__a							)
											self.assertIsNotNone(__a							)






def   lowerCamelCase							():
					__a			:       List[Any]					    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
					return image





@require_tf
@require_vision
class 							__UpperCamelCase				(		unittest.TestCase					):




	@cached_property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

						__a			:       Tuple					    =  self.default_image_processor
						__a			:       Any					    =  prepare_img()
						__a			:       int					    =  image_processor(images=__a						,				return_tensors='tf'							)

						# forward pass
						__a			:       Any					    =  model(**__a							)

						# verify the logits
						__a			:       Any					    =  tf.TensorShape((1, 1000)							)
						self.assertEqual(outputs.logits.shape						,				__a							)

						__a			:       Optional[Any]					    =  tf.constant([0.9285, 0.9015, -0.3150]							)
						self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()						,				__a						,				atol=1E-4							)							)



