
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 							A__		:
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=1_3    ,  __magic_name__=7    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=False    ,  __magic_name__=False    ,  __magic_name__=False    ,  __magic_name__=2    ,  __magic_name__=9_9    ,  __magic_name__=0    ,  __magic_name__=3_2    ,  __magic_name__=5    ,  __magic_name__=4    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=2    ,  __magic_name__=4    ,  __magic_name__="last"    ,  __magic_name__=True    ,  __magic_name__=None    ,  __magic_name__=0    ,  ):
       lowerCamelCase		:	List[str]							=      parent
       lowerCamelCase		:	int							=      batch_size
       lowerCamelCase		:	str							=      seq_length
       lowerCamelCase		:	Optional[int]							=      is_training
       lowerCamelCase		:	Any							=      use_input_lengths
       lowerCamelCase		:	Optional[int]							=      use_token_type_ids
       lowerCamelCase		:	int							=      use_labels
       lowerCamelCase		:	Dict							=      gelu_activation
       lowerCamelCase		:	int							=      sinusoidal_embeddings
       lowerCamelCase		:	str							=      causal
       lowerCamelCase		:	Union[str, Any]							=      asm
       lowerCamelCase		:	Optional[Any]							=      n_langs
       lowerCamelCase		:	Union[str, Any]							=      vocab_size
       lowerCamelCase		:	List[str]							=      n_special
       lowerCamelCase		:	Tuple							=      hidden_size
       lowerCamelCase		:	Any							=      num_hidden_layers
       lowerCamelCase		:	Dict							=      num_attention_heads
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Dict							=      attention_probs_dropout_prob
       lowerCamelCase		:	Tuple							=      max_position_embeddings
       lowerCamelCase		:	Optional[Any]							=      type_sequence_label_size
       lowerCamelCase		:	List[Any]							=      initializer_range
       lowerCamelCase		:	Tuple							=      num_labels
       lowerCamelCase		:	List[str]							=      num_choices
       lowerCamelCase		:	str							=      summary_type
       lowerCamelCase		:	int							=      use_proj
       lowerCamelCase		:	Dict							=      scope
       lowerCamelCase		:	Optional[int]							=      bos_token_id
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size      )
       lowerCamelCase		:	List[Any]							=      random_attention_mask([self.batch_size, self.seq_length]      )

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

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

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

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

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

       lowerCamelCase		:	List[str]							=      model(__a      )

       lowerCamelCase		:	Optional[int]							=      model(__a    ,  start_positions=__a    ,  end_positions=__a      )
       lowerCamelCase		:	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    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  ):
       lowerCamelCase		:	Any							=      XLMForQuestionAnswering(__a      )
       model.to(__a      )
       model.eval()

       lowerCamelCase		:	Dict							=      model(__a      )

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

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

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

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

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

       lowerCamelCase		:	Optional[int]							=      model(__a      )
       lowerCamelCase		:	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    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  ):
       lowerCamelCase		:	List[Any]							=      self.num_labels
       lowerCamelCase		:	Any							=      XLMForTokenClassification(__a      )
       model.to(__a      )
       model.eval()

       lowerCamelCase		:	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    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  ):
       lowerCamelCase		:	str							=      self.num_choices
       lowerCamelCase		:	Union[str, Any]							=      XLMForMultipleChoice(config=__a      )
       model.to(__a      )
       model.eval()
       lowerCamelCase		:	int							=      input_ids.unsqueeze(1      ).expand(-1    ,  self.num_choices    ,  -1      ).contiguous()
       lowerCamelCase		:	Any							=      token_type_ids.unsqueeze(1      ).expand(-1    ,  self.num_choices    ,  -1      ).contiguous()
       lowerCamelCase		:	Optional[Any]							=      input_mask.unsqueeze(1      ).expand(-1    ,  self.num_choices    ,  -1      ).contiguous()
       lowerCamelCase		:	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      ):
       lowerCamelCase		:	List[Any]							=      self.prepare_config_and_inputs()
       (
           lowerCamelCase				
       )		:	Optional[int]							=      config_and_inputs
       lowerCamelCase		:	Tuple							=      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
       return config, inputs_dict





@require_torch
class 							A__		(	lowerCAmelCase_  ,   lowerCAmelCase_  ,   lowerCAmelCase_  ,   unittest.TestCase):
      _UpperCAmelCase				:    Optional[Any]           =			(
          (
              XLMModel,
              XLMWithLMHeadModel,
              XLMForQuestionAnswering,
              XLMForSequenceClassification,
              XLMForQuestionAnsweringSimple,
              XLMForTokenClassification,
              XLMForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      _UpperCAmelCase				:    int           =			(
          (XLMWithLMHeadModel,) if is_torch_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      _UpperCAmelCase				:    List[Any]           =			(
          {
              """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    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):





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

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

       return inputs_dict
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      XLMModelTester(self      )
       lowerCamelCase		:	Union[str, Any]							=      ConfigTester(self    ,  config_class=__a    ,  emb_dim=3_7      )
      def 						UpperCamelCase__							(      self      ):
       self.config_tester.run_common_tests()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_model(*__a      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_lm_head(*__a      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_simple_qa(*__a      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_qa(*__a      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_sequence_classif(*__a      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_token_classif(*__a      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_multiple_choice(*__a      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__=False    ,  __magic_name__=1      ):
       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
        lowerCamelCase		:	Optional[Any]							=      min_length + idx + 1
        lowerCamelCase		:	List[str]							=      min_length + idx + 1

        lowerCamelCase		:	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    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__=False    ,  __magic_name__=1      ):
       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
        lowerCamelCase		:	List[str]							=      min_length + idx + 1
        lowerCamelCase		:	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      ):





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





@require_torch
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""      )
       model.to(__a      )
       lowerCamelCase		:	Union[str, Any]							=      torch.tensor([[1_4, 4_4_7]]    ,  dtype=torch.long    ,  device=__a      )  # the president
       lowerCamelCase		:	int							=      [
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
       ]  # 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
       lowerCamelCase		:	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							)							)



