
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
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.models.swin.modeling_tf_swin import (
			    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFSwinForImageClassification,
			    TFSwinForMaskedImageModeling,
			    TFSwinModel,
			)


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor




class      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



	def __init__(						self			:					Dict    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[Any]=13    ,						lowercase			:					List[str]=32    ,						lowercase			:					Union[str, Any]=2    ,						lowercase			:					Dict=3    ,						lowercase			:					Union[str, Any]=16    ,						lowercase			:					Optional[Any]=[1, 2, 1]    ,						lowercase			:					List[str]=[2, 2, 4]    ,						lowercase			:					Dict=2    ,						lowercase			:					str=2.0    ,						lowercase			:					Optional[Any]=True    ,						lowercase			:					Dict=0.0    ,						lowercase			:					Dict=0.0    ,						lowercase			:					Optional[Any]=0.1    ,						lowercase			:					Union[str, Any]="gelu"    ,						lowercase			:					Optional[Any]=False    ,						lowercase			:					Optional[int]=True    ,						lowercase			:					Tuple=0.02    ,						lowercase			:					Any=1E-5    ,						lowercase			:					Any=True    ,						lowercase			:					Any=None    ,						lowercase			:					List[Any]=True    ,						lowercase			:					Any=10    ,						lowercase			:					Dict=8    ,						):


				'''simple docstring'''


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



	def 				A							(						self			:					str			):


				'''simple docstring'''


				_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 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				return SwinConfig(
				    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    ,						)



	def 				A							(						self			:					List[Any]    ,						lowercase			:					List[str]    ,						lowercase			:					Any    ,						lowercase			:					Any			):


				'''simple docstring'''


				_snake_case						=    TFSwinModel(config=lowercase			)
				_snake_case						=    model(lowercase			)

				_snake_case						=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
				_snake_case						=    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 				A							(						self			:					Optional[Any]    ,						lowercase			:					List[Any]    ,						lowercase			:					Any    ,						lowercase			:					str			):


				'''simple docstring'''


				_snake_case						=    TFSwinForMaskedImageModeling(config=lowercase			)
				_snake_case						=    model(lowercase			)
				self.parent.assertEqual(
				    result.logits.shape    ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

				# test greyscale images
				_snake_case						=    1
				_snake_case						=    TFSwinForMaskedImageModeling(lowercase			)

				_snake_case						=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
				_snake_case						=    model(lowercase			)
				self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, 1, self.image_size, self.image_size)			)



	def 				A							(						self			:					Optional[int]    ,						lowercase			:					Union[str, Any]    ,						lowercase			:					Optional[int]    ,						lowercase			:					Union[str, Any]			):


				'''simple docstring'''


				_snake_case						=    self.type_sequence_label_size
				_snake_case						=    TFSwinForImageClassification(lowercase			)
				_snake_case						=    model(lowercase    ,						labels=lowercase			)
				self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)			)

				# test greyscale images
				_snake_case						=    1
				_snake_case						=    TFSwinForImageClassification(lowercase			)
				_snake_case						=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
				_snake_case						=    model(lowercase			)
				self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)			)





	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				_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      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase							,UpperCAmelCase							,unittest.TestCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Union[str, Any]					     =      (
	    (
	        TFSwinModel,
	        TFSwinForImageClassification,
	        TFSwinForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	_UpperCAmelCase :	Optional[Any]					     =      (
	    {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
	    if is_tf_available()
	    else {}
	)

	_UpperCAmelCase :	Tuple					     =      False
	_UpperCAmelCase :	Optional[int]					     =      False
	_UpperCAmelCase :	Tuple					     =      False
	_UpperCAmelCase :	List[Any]					     =      False



	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    TFSwinModelTester(self			)
				_snake_case						=    ConfigTester(self    ,						config_class=lowercase    ,						embed_dim=37			)



	def 				A							(						self			:					Optional[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 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				return



	def 				A							(						self			:					str			):


				'''simple docstring'''


				_snake_case						=    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase			)



	def 				A							(						self			:					str			):


				'''simple docstring'''


				_snake_case						=    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*lowercase			)



	def 				A							(						self			:					Union[str, Any]			):


				'''simple docstring'''


				_snake_case						=    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase			)



	@unittest.skip(reason='Swin does not use inputs_embeds'			)
	def 				A							(						self			:					Union[str, Any]			):


				'''simple docstring'''


				pass



	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_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(lowercase			)
							self.assertIsInstance(model.get_input_embeddings()    ,						tf.keras.layers.Layer			)
							_snake_case						=    model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowercase    ,						tf.keras.layers.Dense			)			)



	def 				A							(						self			:					str			):


				'''simple docstring'''


				_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(lowercase			)
							_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]    ,						lowercase			)



	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_snake_case			,      _snake_case						=    self.model_tester.prepare_config_and_inputs_for_common()
				_snake_case						=    True

				for model_class in self.all_model_classes:
							_snake_case						=    True
							_snake_case						=    False
							_snake_case						=    True
							_snake_case						=    model_class(lowercase			)
							_snake_case						=    model(**self._prepare_for_class(lowercase    ,						lowercase			)			)
							_snake_case						=    outputs.attentions
							_snake_case						=    len(self.model_tester.depths			)
							self.assertEqual(len(lowercase			)    ,						lowercase			)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							_snake_case						=    True
							_snake_case						=    config.window_size**2
							_snake_case						=    model_class(lowercase			)
							_snake_case						=    model(**self._prepare_for_class(lowercase    ,						lowercase			)			)
							_snake_case						=    outputs.attentions
							self.assertEqual(len(lowercase			)    ,						lowercase			)

							self.assertListEqual(
							    list(attentions[0].shape[-3:]			)    ,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,						)
							_snake_case						=    len(lowercase			)

							# Check attention is always last and order is fine
							_snake_case						=    True
							_snake_case						=    True
							_snake_case						=    model_class(lowercase			)
							_snake_case						=    model(**self._prepare_for_class(lowercase    ,						lowercase			)			)

							if hasattr(self.model_tester    ,						'num_hidden_states_types'			):
										_snake_case						=    self.model_tester.num_hidden_states_types
							else:
										# also another +1 for reshaped_hidden_states
										_snake_case						=    2
							self.assertEqual(out_len + added_hidden_states    ,						len(lowercase			)			)

							_snake_case						=    outputs.attentions

							self.assertEqual(len(lowercase			)    ,						lowercase			)

							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]			)    ,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,						)



	def 				A							(						self			:					str    ,						lowercase			:					Optional[int]    ,						lowercase			:					str    ,						lowercase			:					Tuple    ,						lowercase			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    model_class(lowercase			)
				_snake_case						=    model(**self._prepare_for_class(lowercase    ,						lowercase			)			)
				_snake_case						=    outputs.hidden_states

				_snake_case						=    getattr(
				    self.model_tester    ,						'expected_num_hidden_layers'    ,						len(self.model_tester.depths			) + 1			)
				self.assertEqual(len(lowercase			)    ,						lowercase			)

				# Swin has a different seq_length
				_snake_case						=    to_atuple(config.patch_size			)

				_snake_case						=    (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]    ,						)

				_snake_case						=    outputs.reshaped_hidden_states
				self.assertEqual(len(lowercase			)    ,						lowercase			)

				_snake_case			,      _snake_case			,      _snake_case			,      _snake_case						=    reshaped_hidden_states[0].shape

				_snake_case						=    tf.reshape(reshaped_hidden_states[0]    ,						(batch_size, num_channels, height * width)			)
				_snake_case						=    tf.transpose(lowercase    ,						(0, 2, 1)			)

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



	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_snake_case			,      _snake_case						=    self.model_tester.prepare_config_and_inputs_for_common()

				_snake_case						=    to_atuple(self.model_tester.image_size			)

				for model_class in self.all_model_classes:
							_snake_case						=    True
							self.check_hidden_states_output(lowercase    ,						lowercase    ,						lowercase    ,						lowercase			)

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

							self.check_hidden_states_output(lowercase    ,						lowercase    ,						lowercase    ,						lowercase			)



	def 				A							(						self			:					Tuple			):


				'''simple docstring'''


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

				_snake_case						=    to_atuple(self.model_tester.image_size			)
				_snake_case						=    to_atuple(config.patch_size			)

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

				for model_class in self.all_model_classes:
							_snake_case						=    True
							self.check_hidden_states_output(lowercase    ,						lowercase    ,						lowercase    ,						(padded_height, padded_width)			)

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





	@slow
	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							_snake_case						=    TFSwinModel.from_pretrained(lowercase			)
							self.assertIsNotNone(lowercase			)




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





	'''simple docstring'''



	@cached_property
	def 				A							(						self			:					Tuple			):


				'''simple docstring'''


				return (
				    AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'			)
				    if is_vision_available()
				    else None
				)





	@slow
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'			)
				_snake_case						=    self.default_image_processor

				_snake_case						=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
				_snake_case						=    image_processor(images=lowercase    ,						return_tensors='tf'			)

				# forward pass
				_snake_case						=    model(lowercase			)

				# verify the logits
				_snake_case						=    tf.TensorShape((1, 1_000)			)
				self.assertEqual(outputs.logits.shape    ,						lowercase			)
				_snake_case						=    tf.constant([-0.0948, -0.6454, -0.0921]			)
				self.assertTrue(np.allclose(outputs.logits[0, :3]    ,						lowercase    ,						atol=1E-4			)			)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_lowerCamelCase :       Optional[int]     					=    {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       List[str]     					=    ['''MBart50Tokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       Optional[int]     					=    ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mbartaa import MBartaaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
			import sys

			_lowerCamelCase :       int     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)