
import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
						from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import GLPNImageProcessor







class 							UpperCAmelCase	(   _a					):

					def        _lowerCAmelCase	(			self      :					Optional[int]					):
											lowercase       :    List[str]											=  self.config_class(**self.inputs_dict					)
											self.parent.assertTrue(hasattr(lowerCamelCase__ ,      '''hidden_sizes'''					)					)
											self.parent.assertTrue(hasattr(lowerCamelCase__ ,      '''num_attention_heads'''					)					)
											self.parent.assertTrue(hasattr(lowerCamelCase__ ,      '''num_encoder_blocks'''					)					)







class 							UpperCAmelCase	:

					def __init__(			self      :					List[Any] ,      lowerCAmelCase      :					List[Any] ,      lowerCAmelCase      :					Dict=13 ,      lowerCAmelCase      :					Optional[int]=64 ,      lowerCAmelCase      :					List[Any]=3 ,      lowerCAmelCase      :					int=4 ,      lowerCAmelCase      :					str=[2, 2, 2, 2] ,      lowerCAmelCase      :					Dict=[8, 4, 2, 1] ,      lowerCAmelCase      :					int=[16, 32, 64, 128] ,      lowerCAmelCase      :					Tuple=[1, 4, 8, 16] ,      lowerCAmelCase      :					str=[1, 2, 4, 8] ,      lowerCAmelCase      :					List[Any]=True ,      lowerCAmelCase      :					Dict=True ,      lowerCAmelCase      :					Dict="gelu" ,      lowerCAmelCase      :					Tuple=0.1 ,      lowerCAmelCase      :					Dict=0.1 ,      lowerCAmelCase      :					Optional[int]=0.02 ,      lowerCAmelCase      :					Optional[Any]=16 ,      lowerCAmelCase      :					Tuple=3 ,      lowerCAmelCase      :					Any=None ,      ):
											lowercase       :    Dict											=  parent
											lowercase       :    Dict											=  batch_size
											lowercase       :    List[Any]											=  image_size
											lowercase       :    Dict											=  num_channels
											lowercase       :    Union[str, Any]											=  num_encoder_blocks
											lowercase       :    List[str]											=  sr_ratios
											lowercase       :    Optional[int]											=  depths
											lowercase       :    str											=  hidden_sizes
											lowercase       :    List[str]											=  downsampling_rates
											lowercase       :    Optional[Any]											=  num_attention_heads
											lowercase       :    Optional[Any]											=  is_training
											lowercase       :    int											=  use_labels
											lowercase       :    Optional[Any]											=  hidden_act
											lowercase       :    Dict											=  hidden_dropout_prob
											lowercase       :    str											=  attention_probs_dropout_prob
											lowercase       :    int											=  initializer_range
											lowercase       :    Optional[int]											=  decoder_hidden_size
											lowercase       :    Dict											=  num_labels
											lowercase       :    Any											=  scope

					def        _lowerCAmelCase	(			self      :					Union[str, Any]					):
											lowercase       :    int											=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

											lowercase       :    Any											=  None
											if self.use_labels:
																	lowercase       :    int											=  ids_tensor([self.batch_size, self.image_size, self.image_size] ,      self.num_labels					)

											lowercase       :    int											=  self.get_config()
											return config, pixel_values, labels

					def        _lowerCAmelCase	(			self      :					List[str]					):
											return GLPNConfig(
											    image_size=self.image_size ,      num_channels=self.num_channels ,      num_encoder_blocks=self.num_encoder_blocks ,      depths=self.depths ,      hidden_sizes=self.hidden_sizes ,      num_attention_heads=self.num_attention_heads ,      hidden_act=self.hidden_act ,      hidden_dropout_prob=self.hidden_dropout_prob ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob ,      initializer_range=self.initializer_range ,      decoder_hidden_size=self.decoder_hidden_size ,      )

					def        _lowerCAmelCase	(			self      :					Optional[Any] ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					Optional[int] ,      lowerCAmelCase      :					Any					):
											lowercase       :    Tuple											=  GLPNModel(config=lowerCamelCase__					)
											model.to(lowerCamelCase__					)
											model.eval()
											lowercase       :    Any											=  model(lowerCamelCase__					)
											lowercase       :    List[str]											=  self.image_size // (self.downsampling_rates[-1] * 2)
											self.parent.assertEqual(
											    result.last_hidden_state.shape ,      (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)					)

					def        _lowerCAmelCase	(			self      :					str ,      lowerCAmelCase      :					Optional[int] ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					int					):
											lowercase       :    Dict											=  self.num_labels
											lowercase       :    Optional[int]											=  GLPNForDepthEstimation(lowerCamelCase__					)
											model.to(lowerCamelCase__					)
											model.eval()
											lowercase       :    str											=  model(lowerCamelCase__					)
											self.parent.assertEqual(result.predicted_depth.shape ,      (self.batch_size, self.image_size, self.image_size)					)
											lowercase       :    Any											=  model(lowerCamelCase__ ,      labels=lowerCamelCase__					)
											self.parent.assertEqual(result.predicted_depth.shape ,      (self.batch_size, self.image_size, self.image_size)					)




					def        _lowerCAmelCase	(			self      :					List[Any]					):
											lowercase       :    Union[str, Any]											=  self.prepare_config_and_inputs()
											lowercase       :    List[Any]											=  config_and_inputs
											lowercase       :    List[str]											=  {"pixel_values": pixel_values}
											return config, inputs_dict







@require_torch
class 							UpperCAmelCase	(   _a						, _a						, unittest.TestCase					):
					a__: Any			  =				(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
					a__: Optional[Any]			  =				(
					    {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
					)

					a__: List[Any]			  =				False
					a__: Tuple			  =				False
					a__: Union[str, Any]			  =				False

					def        _lowerCAmelCase	(			self      :					List[str]					):
											lowercase       :    Union[str, Any]											=  GLPNModelTester(self					)
											lowercase       :    List[str]											=  GLPNConfigTester(self ,      config_class=lowerCamelCase__					)

					def        _lowerCAmelCase	(			self      :					Union[str, Any]					):
											self.config_tester.run_common_tests()

					def        _lowerCAmelCase	(			self      :					Dict					):
											lowercase       :    Tuple											=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowerCamelCase__					)

					def        _lowerCAmelCase	(			self      :					List[Any]					):
											lowercase       :    str											=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__					)

					@unittest.skip('''GLPN does not use inputs_embeds'''					)
					def        _lowerCAmelCase	(			self      :					int					):
											pass

					@unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''					)
					def        _lowerCAmelCase	(			self      :					str					):
											pass

					def        _lowerCAmelCase	(			self      :					Union[str, Any]					):
											lowercase       :    Union[str, Any]											=  self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowercase       :    Union[str, Any]											=  model_class(lowerCamelCase__					)
																	lowercase       :    List[Any]											=  inspect.signature(model.forward					)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowercase       :    Any											=  [*signature.parameters.keys()]

																	lowercase       :    str											=  ["pixel_values"]
																	self.assertListEqual(arg_names[:1] ,      lowerCamelCase__					)

					def        _lowerCAmelCase	(			self      :					Optional[Any]					):
											lowercase       :    Any											=  self.model_tester.prepare_config_and_inputs_for_common()
											lowercase       :    Optional[int]											=  True

											for model_class in self.all_model_classes:
																	lowercase       :    str											=  True
																	lowercase       :    Optional[int]											=  False
																	lowercase       :    Union[str, Any]											=  True
																	lowercase       :    List[Any]											=  model_class(lowerCamelCase__					)
																	model.to(lowerCamelCase__					)
																	model.eval()
																	with torch.no_grad():
																							lowercase       :    List[str]											=  model(**self._prepare_for_class(lowerCamelCase__ ,      lowerCamelCase__					)					)
																	lowercase       :    Optional[Any]											=  outputs.attentions

																	lowercase       :    Union[str, Any]											=  sum(self.model_tester.depths					)
																	self.assertEqual(len(lowerCamelCase__					) ,      lowerCamelCase__					)

																	# check that output_attentions also work using config
																	del inputs_dict["output_attentions"]
																	lowercase       :    Tuple											=  True
																	lowercase       :    List[str]											=  model_class(lowerCamelCase__					)
																	model.to(lowerCamelCase__					)
																	model.eval()
																	with torch.no_grad():
																							lowercase       :    int											=  model(**self._prepare_for_class(lowerCamelCase__ ,      lowerCamelCase__					)					)
																	lowercase       :    Dict											=  outputs.attentions

																	self.assertEqual(len(lowerCamelCase__					) ,      lowerCamelCase__					)

																	# verify the first attentions (first block, first layer)
																	lowercase       :    Optional[Any]											=  (self.model_tester.image_size // 4) ** 2
																	lowercase       :    Any											=  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]					) ,      [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,      )

																	# verify the last attentions (last block, last layer)
																	lowercase       :    int											=  (self.model_tester.image_size // 32) ** 2
																	lowercase       :    Tuple											=  (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
																	self.assertListEqual(
																	    list(attentions[-1].shape[-3:]					) ,      [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] ,      )
																	lowercase       :    Tuple											=  len(lowerCamelCase__					)

																	# Check attention is always last and order is fine
																	lowercase       :    str											=  True
																	lowercase       :    int											=  True
																	lowercase       :    Any											=  model_class(lowerCamelCase__					)
																	model.to(lowerCamelCase__					)
																	model.eval()
																	with torch.no_grad():
																							lowercase       :    Union[str, Any]											=  model(**self._prepare_for_class(lowerCamelCase__ ,      lowerCamelCase__					)					)

																	self.assertEqual(out_len + 1 ,      len(lowerCamelCase__					)					)

																	lowercase       :    Union[str, Any]											=  outputs.attentions

																	self.assertEqual(len(lowerCamelCase__					) ,      lowerCamelCase__					)
																	# verify the first attentions (first block, first layer)
																	lowercase       :    int											=  (self.model_tester.image_size // 4) ** 2
																	lowercase       :    List[str]											=  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																	self.assertListEqual(
																	    list(self_attentions[0].shape[-3:]					) ,      [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,      )

					def        _lowerCAmelCase	(			self      :					Optional[Any]					):
											def check_hidden_states_output(lowerCAmelCase      :					List[str] ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					Dict					):
																	lowercase       :    int											=  model_class(lowerCamelCase__					)
																	model.to(lowerCamelCase__					)
																	model.eval()

																	with torch.no_grad():
																							lowercase       :    Dict											=  model(**self._prepare_for_class(lowerCamelCase__ ,      lowerCamelCase__					)					)

																	lowercase       :    str											=  outputs.hidden_states

																	lowercase       :    Union[str, Any]											=  self.model_tester.num_encoder_blocks
																	self.assertEqual(len(lowerCamelCase__					) ,      lowerCamelCase__					)

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

											lowercase       :    Union[str, Any]											=  self.model_tester.prepare_config_and_inputs_for_common()

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

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	lowercase       :    str											=  True

																	check_hidden_states_output(lowerCamelCase__ ,      lowerCamelCase__ ,      lowerCamelCase__					)

					def        _lowerCAmelCase	(			self      :					List[Any]					):
											if not self.model_tester.is_training:
																	return

											lowercase       :    Union[str, Any]											=  self.model_tester.prepare_config_and_inputs_for_common()
											lowercase       :    List[Any]											=  True

											for model_class in self.all_model_classes:
																	if model_class in get_values(lowerCamelCase__					):
																							continue
																	# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
																	# this can then be incorporated into _prepare_for_class in test_modeling_common.py
																	if model_class.__name__ == "GLPNForDepthEstimation":
																							lowercase       :    int											=  inputs_dict["pixel_values"].shape
																							lowercase       :    Optional[int]											=  torch.zeros(
																							    [self.model_tester.batch_size, height, width] ,      device=lowerCamelCase__					).long()
																	lowercase       :    Optional[Any]											=  model_class(lowerCamelCase__					)
																	model.to(lowerCamelCase__					)
																	model.train()
																	lowercase       :    Any											=  self._prepare_for_class(lowerCamelCase__ ,      lowerCamelCase__ ,      return_labels=lowerCamelCase__					)
																	lowercase       :    Union[str, Any]											=  model(**lowerCamelCase__					).loss
																	loss.backward()




					@slow
					def        _lowerCAmelCase	(			self      :					int					):
											for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase       :    List[Any]											=  GLPNModel.from_pretrained(lowerCamelCase__					)
																	self.assertIsNotNone(lowerCamelCase__					)





def        lowerCamelCase_      (			):
						lowercase       :    Any											=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
						return image







@require_torch
@require_vision
@slow
class 							UpperCAmelCase	(   unittest.TestCase					):

					@slow
					def        _lowerCAmelCase	(			self      :					int					):
											lowercase       :    List[str]											=  GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
											lowercase       :    List[str]											=  GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(lowerCamelCase__					)

											lowercase       :    str											=  prepare_img()
											lowercase       :    Tuple											=  image_processor(images=lowerCamelCase__ ,      return_tensors='''pt'''					).to(lowerCamelCase__					)

											# forward pass
											with torch.no_grad():
																	lowercase       :    int											=  model(**lowerCamelCase__					)

											# verify the predicted depth
											lowercase       :    Any											=  torch.Size([1, 480, 640]					)
											self.assertEqual(outputs.predicted_depth.shape ,      lowerCamelCase__					)

											lowercase       :    int											=  torch.tensor(
											    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]					).to(lowerCamelCase__					)

											self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3] ,      lowerCamelCase__ ,      atol=1E-4					)					)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__  							=							{"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				snake_case__  							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


