






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_a      	=  {
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LongT5EncoderModel''',
		    '''LongT5ForConditionalGeneration''',
		    '''LongT5Model''',
		    '''LongT5PreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''FlaxLongT5ForConditionalGeneration''',
		    '''FlaxLongT5Model''',
		    '''FlaxLongT5PreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_longta import (
				    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LongTaEncoderModel,
				    LongTaForConditionalGeneration,
				    LongTaModel,
				    LongTaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_longta import (
				    FlaxLongTaForConditionalGeneration,
				    FlaxLongTaModel,
				    FlaxLongTaPreTrainedModel,
				)


else:
		import sys

		_a      	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''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							)							)



