"""simple docstring"""



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
           TFDeiTForImageClassification,
           TFDeiTForImageClassificationWithTeacher,
           TFDeiTForMaskedImageModeling,
           TFDeiTModel,
       )
       from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import DeiTImageProcessor



class   _A						:




    """simple docstring"""




    def __init__(		self				:  Tuple						,   A_				:  int						,   A_				:  Union[str, Any]=13						,   A_				:  Union[str, Any]=30						,   A_				:  str=2						,   A_				:  int=3						,   A_				:  List[str]=True						,   A_				:  List[Any]=True						,   A_				:  Any=32						,   A_				:  Optional[int]=2						,   A_				:  Dict=4						,   A_				:  Union[str, Any]=37						,   A_				:  Optional[Any]="gelu"						,   A_				:  str=0.1						,   A_				:  int=0.1						,   A_				:  Union[str, Any]=10						,   A_				:  int=0.02						,   A_				:  Dict=3						,   A_				:  List[Any]=None						,   A_				:  Dict=2						,   )     ->							Dict:
         __snake_case				=				parent
         __snake_case				=				batch_size
         __snake_case				=				image_size
         __snake_case				=				patch_size
         __snake_case				=				num_channels
         __snake_case				=				is_training
         __snake_case				=				use_labels
         __snake_case				=				hidden_size
         __snake_case				=				num_hidden_layers
         __snake_case				=				num_attention_heads
         __snake_case				=				intermediate_size
         __snake_case				=				hidden_act
         __snake_case				=				hidden_dropout_prob
         __snake_case				=				attention_probs_dropout_prob
         __snake_case				=				type_sequence_label_size
         __snake_case				=				initializer_range
         __snake_case				=				scope
         __snake_case				=				encoder_stride

         # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
         __snake_case				=				(image_size // patch_size) ** 2
         __snake_case				=				num_patches + 2
    def 			lowercase       (		self				:  List[Any]     )     ->							Union[str, Any]:
         __snake_case				=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

         __snake_case				=				self.get_config()

         return config, pixel_values, labels
    def 			lowercase       (		self				:  List[Any]     )     ->							Optional[int]:
         return DeiTConfig(
             image_size=self.image_size						,   patch_size=self.patch_size						,   num_channels=self.num_channels						,   hidden_size=self.hidden_size						,   num_hidden_layers=self.num_hidden_layers						,   num_attention_heads=self.num_attention_heads						,   intermediate_size=self.intermediate_size						,   hidden_act=self.hidden_act						,   hidden_dropout_prob=self.hidden_dropout_prob						,   attention_probs_dropout_prob=self.attention_probs_dropout_prob						,   is_decoder=snake_case__						,   initializer_range=self.initializer_range						,   encoder_stride=self.encoder_stride						,   )
    def 			lowercase       (		self				:  List[Any]						,   A_				:  str						,   A_				:  int						,   A_				:  Dict     )     ->							List[Any]:
         __snake_case				=				TFDeiTModel(config=snake_case__     )
         __snake_case				=				model(snake_case__     )
         self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)     )
    def 			lowercase       (		self				:  List[Any]						,   A_				:  Optional[int]						,   A_				:  Tuple						,   A_				:  List[str]     )     ->							Union[str, Any]:
         __snake_case				=				TFDeiTForMaskedImageModeling(config=snake_case__     )
         __snake_case				=				model(snake_case__     )
         self.parent.assertEqual(
             result.reconstruction.shape						,   (self.batch_size, self.num_channels, self.image_size, self.image_size)     )

         # test greyscale images
         __snake_case				=				1
         __snake_case				=				TFDeiTForMaskedImageModeling(snake_case__     )

         __snake_case				=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
         __snake_case				=				model(snake_case__     )
         self.parent.assertEqual(result.reconstruction.shape						,   (self.batch_size, 1, self.image_size, self.image_size)     )
    def 			lowercase       (		self				:  Union[str, Any]						,   A_				:  int						,   A_				:  Any						,   A_				:  str     )     ->							Dict:
         __snake_case				=				self.type_sequence_label_size
         __snake_case				=				TFDeiTForImageClassification(snake_case__     )
         __snake_case				=				model(snake_case__						,   labels=snake_case__     )
         self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.type_sequence_label_size)     )

         # test greyscale images
         __snake_case				=				1
         __snake_case				=				TFDeiTForImageClassification(snake_case__     )

         __snake_case				=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
         __snake_case				=				model(snake_case__						,   labels=snake_case__     )
         self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.type_sequence_label_size)     )







    def 			lowercase       (		self				:  Tuple     )     ->							Dict:
         __snake_case				=				self.prepare_config_and_inputs()
         __snake_case		,						__snake_case		,						__snake_case				=				config_and_inputs
         __snake_case				=				{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_tf
class   _A						(    SCREAMING_SNAKE_CASE__				,			SCREAMING_SNAKE_CASE__				,			unittest.TestCase       ):




    """simple docstring"""




    UpperCamelCase_						:       Optional[int]      					=       (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    UpperCamelCase_						:       str      					=       (
        {
            '''feature-extraction''': TFDeiTModel,
            '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    UpperCamelCase_						:       List[str]      					=       False
    UpperCamelCase_						:       Optional[Any]      					=       False
    UpperCamelCase_						:       Any      					=       False
    UpperCamelCase_						:       int      					=       False
    def 			lowercase       (		self				:  Optional[Any]     )     ->							Tuple:
         __snake_case				=				TFDeiTModelTester(self     )
         __snake_case				=				ConfigTester(self						,   config_class=snake_case__						,   has_text_modality=snake_case__						,   hidden_size=37     )
    def 			lowercase       (		self				:  Tuple     )     ->							List[Any]:
         self.config_tester.run_common_tests()
    @unittest.skip(reason='''DeiT does not use inputs_embeds'''     )
    def 			lowercase       (		self				:  Dict     )     ->							Dict:
         pass
    def 			lowercase       (		self				:  int     )     ->							Any:
         __snake_case		,						__snake_case				=				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __snake_case				=				model_class(snake_case__     )
              self.assertIsInstance(model.get_input_embeddings()						,   (tf.keras.layers.Layer)     )
              __snake_case				=				model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(snake_case__						,   tf.keras.layers.Dense     )     )
    def 			lowercase       (		self				:  List[Any]     )     ->							int:
         __snake_case		,						__snake_case				=				self.model_tester.prepare_config_and_inputs_for_common()

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

              __snake_case				=				['''pixel_values''']
              self.assertListEqual(arg_names[:1]						,   snake_case__     )
    def 			lowercase       (		self				:  str     )     ->							List[str]:
         __snake_case				=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__     )
    def 			lowercase       (		self				:  str     )     ->							int:
         __snake_case				=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__     )
    def 			lowercase       (		self				:  Tuple     )     ->							List[str]:
         __snake_case				=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case__     )
    def 			lowercase       (		self				:  str						,   A_				:  str						,   A_				:  Any						,   A_				:  Tuple=False     )     ->							Any:
         __snake_case				=				super()._prepare_for_class(snake_case__						,   snake_case__						,   return_labels=snake_case__     )

         if return_labels:
              if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call     ).parameters:
                   del inputs_dict["labels"]

         return inputs_dict







    @slow
    def 			lowercase       (		self				:  List[str]     )     ->							Optional[Any]:

         for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __snake_case				=				TFDeiTModel.from_pretrained(snake_case__     )
              self.assertIsNotNone(snake_case__     )






def  SCREAMING_SNAKE_CASE							(  ):
     __snake_case				=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     return image




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




    """simple docstring"""




    @cached_property
    def 			lowercase       (		self				:  List[Any]     )     ->							int:
         return (
             DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''     )
             if is_vision_available()
             else None
         )







    @slow
    def 			lowercase       (		self				:  str     )     ->							Dict:
         __snake_case				=				TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''     )

         __snake_case				=				self.default_image_processor
         __snake_case				=				prepare_img()
         __snake_case				=				image_processor(images=snake_case__						,   return_tensors='''tf'''     )

         # forward pass
         __snake_case				=				model(**snake_case__     )

         # verify the logits
         __snake_case				=				tf.TensorShape((1, 1_000)     )
         self.assertEqual(outputs.logits.shape						,   snake_case__     )

         __snake_case				=				tf.constant([-1.02_66, 0.19_12, -1.28_61]     )

         self.assertTrue(np.allclose(outputs.logits[0, :3]						,   snake_case__						,   atol=1E-4     )     )





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE:     List[Any]									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Optional[Any]									= {
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="umt5"
						lowerCAmelCase__			      =["past_key_values"]



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   Union[str, Any]=25_01_12					,							snake_case__	:   Optional[int]=5_12					,							snake_case__	:   Optional[Any]=64					,							snake_case__	:   str=10_24					,							snake_case__	:   Dict=8					,							snake_case__	:   Union[str, Any]=None					,							snake_case__	:   List[Any]=6					,							snake_case__	:   List[Any]=32					,							snake_case__	:   List[Any]=1_28					,							snake_case__	:   Optional[int]=0.1					,							snake_case__	:   List[str]=1e-6					,							snake_case__	:   Optional[Any]=1.0					,							snake_case__	:   Optional[int]="gated-gelu"					,							snake_case__	:   Any=True					,							snake_case__	:   List[Any]=True					,							snake_case__	:   List[str]="T5Tokenizer"					,							snake_case__	:   List[str]=True					,							snake_case__	:   Tuple=0					,							snake_case__	:   Optional[int]=1					,							snake_case__	:   List[str]=0					,							**snake_case__	:   Any					,							):

								"""simple docstring"""

								super().__init__(
								    is_encoder_decoder=snake_case__					,							tokenizer_class=snake_case__					,							tie_word_embeddings=snake_case__					,							pad_token_id=snake_case__					,							eos_token_id=snake_case__					,							decoder_start_token_id=snake_case__					,							**snake_case__					,							)
								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							d_model
								SCREAMING_SNAKE_CASE_					      =							d_kv
								SCREAMING_SNAKE_CASE_					      =							d_ff
								SCREAMING_SNAKE_CASE_					      =							num_layers
								SCREAMING_SNAKE_CASE_					      =							(
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								SCREAMING_SNAKE_CASE_					      =							num_heads
								SCREAMING_SNAKE_CASE_					      =							relative_attention_num_buckets
								SCREAMING_SNAKE_CASE_					      =							relative_attention_max_distance
								SCREAMING_SNAKE_CASE_					      =							dropout_rate
								SCREAMING_SNAKE_CASE_					      =							layer_norm_epsilon
								SCREAMING_SNAKE_CASE_					      =							initializer_factor
								SCREAMING_SNAKE_CASE_					      =							feed_forward_proj
								SCREAMING_SNAKE_CASE_					      =							use_cache

								SCREAMING_SNAKE_CASE_					      =							self.feed_forward_proj.split('-'  )
								SCREAMING_SNAKE_CASE_					      =							act_info[-1]
								SCREAMING_SNAKE_CASE_					      =							act_info[0] == 'gated'

								if len(snake_case__  ) > 1 and act_info[0] != "gated" or len(snake_case__  ) > 2:
										raise ValueError(
										    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
										    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
										    '\'gated-gelu\' or \'relu\''  )

								if feed_forward_proj == "gated-gelu":
										SCREAMING_SNAKE_CASE_					      =							'gelu_new'



						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.d_model



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return self.num_heads






						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.num_layers



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{
								    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
								    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
								}
								if self.use_past:
										SCREAMING_SNAKE_CASE_					      =							'past_encoder_sequence + sequence'
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch'}
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
								else:
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'decoder_sequence'}
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'decoder_sequence'}

								if self.use_past:
										self.fill_with_past_key_values_(snake_case__					,							direction='inputs'  )

								return common_inputs



						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return 13






						@property
						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								return 5e-4