
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     __snake_case						(					A__						):




							def        UpperCAmelCase__      (	self					:	str):
									lowerCAmelCase_      :					Any     =			self.config_class(**self.inputs_dict)
									self.parent.assertTrue(hasattr(snake_case_    ,    '''hidden_sizes'''))
									self.parent.assertTrue(hasattr(snake_case_    ,    '''num_attention_heads'''))
									self.parent.assertTrue(hasattr(snake_case_    ,    '''num_encoder_blocks'''))





class     __snake_case						:




							def __init__(	self					:	List[Any]    ,    A_					:	Optional[Any]    ,    A_					:	Tuple=1_3    ,    A_					:	Union[str, Any]=6_4    ,    A_					:	Dict=3    ,    A_					:	Tuple=4    ,    A_					:	List[Any]=[2, 2, 2, 2]    ,    A_					:	str=[8, 4, 2, 1]    ,    A_					:	Tuple=[1_6, 3_2, 6_4, 1_2_8]    ,    A_					:	Union[str, Any]=[1, 4, 8, 1_6]    ,    A_					:	Optional[Any]=[1, 2, 4, 8]    ,    A_					:	Tuple=True    ,    A_					:	Optional[Any]=True    ,    A_					:	List[Any]="gelu"    ,    A_					:	int=0.1    ,    A_					:	int=0.1    ,    A_					:	Optional[Any]=0.02    ,    A_					:	Optional[Any]=1_6    ,    A_					:	Tuple=3    ,    A_					:	str=None    ,    ):
									lowerCAmelCase_      :					List[str]     =			parent
									lowerCAmelCase_      :					str     =			batch_size
									lowerCAmelCase_      :					List[str]     =			image_size
									lowerCAmelCase_      :					Union[str, Any]     =			num_channels
									lowerCAmelCase_      :					List[str]     =			num_encoder_blocks
									lowerCAmelCase_      :					Union[str, Any]     =			sr_ratios
									lowerCAmelCase_      :					Optional[Any]     =			depths
									lowerCAmelCase_      :					int     =			hidden_sizes
									lowerCAmelCase_      :					str     =			downsampling_rates
									lowerCAmelCase_      :					Dict     =			num_attention_heads
									lowerCAmelCase_      :					Union[str, Any]     =			is_training
									lowerCAmelCase_      :					int     =			use_labels
									lowerCAmelCase_      :					Optional[Any]     =			hidden_act
									lowerCAmelCase_      :					str     =			hidden_dropout_prob
									lowerCAmelCase_      :					List[Any]     =			attention_probs_dropout_prob
									lowerCAmelCase_      :					Any     =			initializer_range
									lowerCAmelCase_      :					List[str]     =			decoder_hidden_size
									lowerCAmelCase_      :					Optional[int]     =			num_labels
									lowerCAmelCase_      :					Optional[int]     =			scope




							def        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_      :					Any     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

									lowerCAmelCase_      :					Tuple     =			None
									if self.use_labels:
											lowerCAmelCase_      :					str     =			ids_tensor([self.batch_size, self.image_size, self.image_size]    ,    self.num_labels)

									lowerCAmelCase_      :					Optional[Any]     =			self.get_config()
									return config, pixel_values, labels




							def        UpperCAmelCase__      (	self					:	Dict):
									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        UpperCAmelCase__      (	self					:	int    ,    A_					:	str    ,    A_					:	List[str]    ,    A_					:	List[str]):
									lowerCAmelCase_      :					str     =			GLPNModel(config=snake_case_)
									model.to(snake_case_)
									model.eval()
									lowerCAmelCase_      :					List[Any]     =			model(snake_case_)
									lowerCAmelCase_      :					Optional[int]     =			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        UpperCAmelCase__      (	self					:	Tuple    ,    A_					:	int    ,    A_					:	int    ,    A_					:	Optional[Any]):
									lowerCAmelCase_      :					str     =			self.num_labels
									lowerCAmelCase_      :					Tuple     =			GLPNForDepthEstimation(snake_case_)
									model.to(snake_case_)
									model.eval()
									lowerCAmelCase_      :					List[str]     =			model(snake_case_)
									self.parent.assertEqual(result.predicted_depth.shape    ,    (self.batch_size, self.image_size, self.image_size))
									lowerCAmelCase_      :					Tuple     =			model(snake_case_    ,    labels=snake_case_)
									self.parent.assertEqual(result.predicted_depth.shape    ,    (self.batch_size, self.image_size, self.image_size))







							def        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_      :					Tuple     =			self.prepare_config_and_inputs()
									lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_      :					Union[str, Any]     =			config_and_inputs
									lowerCAmelCase_      :					Any     =			{'''pixel_values''': pixel_values}
									return config, inputs_dict





@require_torch
class     __snake_case						(					A__						,A__						,unittest.TestCase						):
							_a      				=			(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
							_a      				=			(
							    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
							)

							_a      				=			False
							_a      				=			False
							_a      				=			False




							def        UpperCAmelCase__      (	self					:	List[str]):
									lowerCAmelCase_      :					Optional[Any]     =			GLPNModelTester(self)
									lowerCAmelCase_      :					Dict     =			GLPNConfigTester(self    ,    config_class=snake_case_)




							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									self.config_tester.run_common_tests()




							def        UpperCAmelCase__      (	self					:	Any):
									lowerCAmelCase_      :					Tuple     =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case_)




							def        UpperCAmelCase__      (	self					:	int):
									lowerCAmelCase_      :					str     =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*snake_case_)




							@unittest.skip('''GLPN does not use inputs_embeds''')
							def        UpperCAmelCase__      (	self					:	List[str]):
									pass




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




							def        UpperCAmelCase__      (	self					:	Tuple):
									lowerCAmelCase_ ,  lowerCAmelCase_      :					Any     =			self.model_tester.prepare_config_and_inputs_for_common()

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

											lowerCAmelCase_      :					int     =			['''pixel_values''']
											self.assertListEqual(arg_names[:1]    ,    snake_case_)




							def        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[Any]     =			self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase_      :					List[str]     =			True

									for model_class in self.all_model_classes:
											lowerCAmelCase_      :					int     =			True
											lowerCAmelCase_      :					int     =			False
											lowerCAmelCase_      :					Optional[Any]     =			True
											lowerCAmelCase_      :					Tuple     =			model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											with torch.no_grad():
													lowerCAmelCase_      :					Optional[int]     =			model(**self._prepare_for_class(snake_case_    ,    snake_case_))
											lowerCAmelCase_      :					Tuple     =			outputs.attentions

											lowerCAmelCase_      :					List[Any]     =			sum(self.model_tester.depths)
											self.assertEqual(len(snake_case_)    ,    snake_case_)

											# check that output_attentions also work using config
											del inputs_dict["output_attentions"]
											lowerCAmelCase_      :					str     =			True
											lowerCAmelCase_      :					int     =			model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											with torch.no_grad():
													lowerCAmelCase_      :					Optional[Any]     =			model(**self._prepare_for_class(snake_case_    ,    snake_case_))
											lowerCAmelCase_      :					Union[str, Any]     =			outputs.attentions

											self.assertEqual(len(snake_case_)    ,    snake_case_)

											# verify the first attentions (first block, first layer)
											lowerCAmelCase_      :					List[Any]     =			(self.model_tester.image_size // 4) ** 2
											lowerCAmelCase_      :					int     =			(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)
											lowerCAmelCase_      :					Dict     =			(self.model_tester.image_size // 3_2) ** 2
											lowerCAmelCase_      :					List[str]     =			(self.model_tester.image_size // (3_2 * 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]    ,    )
											lowerCAmelCase_      :					Optional[Any]     =			len(snake_case_)

											# Check attention is always last and order is fine
											lowerCAmelCase_      :					Optional[int]     =			True
											lowerCAmelCase_      :					List[str]     =			True
											lowerCAmelCase_      :					Dict     =			model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											with torch.no_grad():
													lowerCAmelCase_      :					Dict     =			model(**self._prepare_for_class(snake_case_    ,    snake_case_))

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

											lowerCAmelCase_      :					Optional[int]     =			outputs.attentions

											self.assertEqual(len(snake_case_)    ,    snake_case_)
											# verify the first attentions (first block, first layer)
											lowerCAmelCase_      :					Any     =			(self.model_tester.image_size // 4) ** 2
											lowerCAmelCase_      :					Dict     =			(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        UpperCAmelCase__      (	self					:	Any):
									def check_hidden_states_output(A_					:	Optional[Any]    ,    A_					:	List[str]    ,    A_					:	Any):
											lowerCAmelCase_      :					Optional[int]     =			model_class(snake_case_)
											model.to(snake_case_)
											model.eval()

											with torch.no_grad():
													lowerCAmelCase_      :					Tuple     =			model(**self._prepare_for_class(snake_case_    ,    snake_case_))

											lowerCAmelCase_      :					List[str]     =			outputs.hidden_states

											lowerCAmelCase_      :					List[Any]     =			self.model_tester.num_encoder_blocks
											self.assertEqual(len(snake_case_)    ,    snake_case_)

											# 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,
											    ]    ,    )

									lowerCAmelCase_ ,  lowerCAmelCase_      :					Tuple     =			self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowerCAmelCase_      :					str     =			True
											check_hidden_states_output(snake_case_    ,    snake_case_    ,    snake_case_)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCAmelCase_      :					List[Any]     =			True

											check_hidden_states_output(snake_case_    ,    snake_case_    ,    snake_case_)




							def        UpperCAmelCase__      (	self					:	List[str]):
									if not self.model_tester.is_training:
											return

									lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[int]     =			self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase_      :					Union[str, Any]     =			True

									for model_class in self.all_model_classes:
											if model_class in get_values(snake_case_):
													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":
													lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_      :					List[str]     =			inputs_dict['''pixel_values'''].shape
													lowerCAmelCase_      :					Optional[int]     =			torch.zeros(
													    [self.model_tester.batch_size, height, width]    ,    device=snake_case_).long()
											lowerCAmelCase_      :					Optional[Any]     =			model_class(snake_case_)
											model.to(snake_case_)
											model.train()
											lowerCAmelCase_      :					int     =			self._prepare_for_class(snake_case_    ,    snake_case_    ,    return_labels=snake_case_)
											lowerCAmelCase_      :					Optional[int]     =			model(**snake_case_).loss
											loss.backward()







							@slow
							def        UpperCAmelCase__      (	self					:	int):
									for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCAmelCase_      :					Tuple     =			GLPNModel.from_pretrained(snake_case_)
											self.assertIsNotNone(snake_case_)





def       UpperCamelCase(  ):
		lowerCAmelCase_      :					Dict     =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
		return image





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




							@slow
							def        UpperCAmelCase__      (	self					:	Dict):
									lowerCAmelCase_      :					Optional[int]     =			GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0])
									lowerCAmelCase_      :					List[str]     =			GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(snake_case_)

									lowerCAmelCase_      :					int     =			prepare_img()
									lowerCAmelCase_      :					Optional[int]     =			image_processor(images=snake_case_    ,    return_tensors='''pt''').to(snake_case_)

									# forward pass
									with torch.no_grad():
											lowerCAmelCase_      :					Dict     =			model(**snake_case_)

									# verify the predicted depth
									lowerCAmelCase_      :					List[Any]     =			torch.Size([1, 4_8_0, 6_4_0])
									self.assertEqual(outputs.predicted_depth.shape    ,    snake_case_)

									lowerCAmelCase_      :					List[Any]     =			torch.tensor(
									    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]).to(snake_case_)

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










import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


A__  :  Tuple       = logging.get_logger(__name__)

A__  :  List[str]       = [
    '''yjernite/retribert-base-uncased''',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]



class     __snake_case						(					UpperCamelCase_						):
							_a      				=			RetriBertConfig
							_a      				=			None
							_a      				=			'''retribert'''




							def        UpperCAmelCase__      (	self					:	Optional[int]    ,    A_					:	Union[str, Any]):
									if isinstance(A_    ,    nn.Linear):
											module.weight.data.normal_(mean=0.0    ,    std=self.config.initializer_range)
											if module.bias is not None:
													module.bias.data.zero_()
									elif isinstance(A_    ,    nn.Embedding):
											module.weight.data.normal_(mean=0.0    ,    std=self.config.initializer_range)
											if module.padding_idx is not None:
													module.weight.data[module.padding_idx].zero_()
									elif isinstance(A_    ,    nn.LayerNorm):
											module.bias.data.zero_()
											module.weight.data.fill_(1.0)





A__  :  str       = R'''

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''



@add_start_docstrings(
    '''Bert Based model to embed queries or document for document retrieval.'''						,UpperCamelCase_						,)
class     __snake_case						(					UpperCamelCase_						):




							def __init__(	self					:	Optional[int]    ,    A_					:	RetriBertConfig):
									super().__init__(A_)
									lowerCAmelCase_      :					Optional[int]     =			config.projection_dim

									lowerCAmelCase_      :					int     =			BertModel(A_)
									lowerCAmelCase_      :					int     =			None if config.share_encoders else BertModel(A_)
									lowerCAmelCase_      :					str     =			nn.Dropout(config.hidden_dropout_prob)
									lowerCAmelCase_      :					Dict     =			nn.Linear(config.hidden_size    ,    config.projection_dim    ,    bias=A_)
									lowerCAmelCase_      :					Any     =			nn.Linear(config.hidden_size    ,    config.projection_dim    ,    bias=A_)

									lowerCAmelCase_      :					int     =			nn.CrossEntropyLoss(reduction='''mean''')

									# Initialize weights and apply final processing
									self.post_init()




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	Any    ,    A_					:	Optional[int]    ,    A_					:	Optional[int]    ,    A_					:	Optional[Any]=-1    ,    ):
									# reproduces BERT forward pass with checkpointing
									if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
											return sent_encoder(A_    ,    attention_mask=A_)[1]
									else:
											# prepare implicit variables
											lowerCAmelCase_      :					Any     =			input_ids.device
											lowerCAmelCase_      :					str     =			input_ids.size()
											lowerCAmelCase_      :					Dict     =			torch.zeros(A_    ,    dtype=torch.long    ,    device=A_)
											lowerCAmelCase_      :					List[str]     =			[None] * sent_encoder.config.num_hidden_layers
											lowerCAmelCase_      :					torch.Tensor     =			sent_encoder.get_extended_attention_mask(
											    A_    ,    A_)

											# define function for checkpointing
											def partial_encode(*A_					:	List[Any]):
													lowerCAmelCase_      :					Optional[Any]     =			sent_encoder.encoder(
													    inputs[0]    ,    attention_mask=inputs[1]    ,    head_mask=A_    ,    )
													lowerCAmelCase_      :					Any     =			encoder_outputs[0]
													lowerCAmelCase_      :					Optional[Any]     =			sent_encoder.pooler(A_)
													return pooled_output

											# run embedding layer on everything at once
											lowerCAmelCase_      :					str     =			sent_encoder.embeddings(
											    input_ids=A_    ,    position_ids=A_    ,    token_type_ids=A_    ,    inputs_embeds=A_)
											# run encoding and pooling on one mini-batch at a time
											lowerCAmelCase_      :					str     =			[]
											for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)):
													lowerCAmelCase_      :					Optional[Any]     =			embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
													lowerCAmelCase_      :					int     =			extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
													lowerCAmelCase_      :					str     =			checkpoint.checkpoint(A_    ,    A_    ,    A_)
													pooled_output_list.append(A_)
											return torch.cat(A_    ,    dim=0)




							def        UpperCAmelCase__      (	self					:	List[str]    ,    A_					:	List[Any]    ,    A_					:	Union[str, Any]=None    ,    A_					:	Dict=-1    ,    ):
									lowerCAmelCase_      :					Optional[int]     =			self.embed_sentences_checkpointed(
									    A_    ,    A_    ,    self.bert_query    ,    A_    ,    )
									return self.project_query(A_)




							def        UpperCAmelCase__      (	self					:	Optional[Any]    ,    A_					:	str    ,    A_					:	int=None    ,    A_					:	Any=-1    ,    ):
									lowerCAmelCase_      :					Union[str, Any]     =			self.embed_sentences_checkpointed(
									    A_    ,    A_    ,    self.bert_query if self.bert_doc is None else self.bert_doc    ,    A_    ,    )
									return self.project_doc(A_)







							def        UpperCAmelCase__      (	self					:	List[Any]    ,    A_					:	torch.LongTensor    ,    A_					:	Optional[torch.FloatTensor]    ,    A_					:	torch.LongTensor    ,    A_					:	Optional[torch.FloatTensor]    ,    A_					:	int = -1    ,    ):
									lowerCAmelCase_      :					List[Any]     =			input_ids_query.device
									lowerCAmelCase_      :					List[str]     =			self.embed_questions(A_    ,    A_    ,    A_)
									lowerCAmelCase_      :					Union[str, Any]     =			self.embed_answers(A_    ,    A_    ,    A_)
									lowerCAmelCase_      :					Any     =			torch.mm(A_    ,    a_reps.t())
									lowerCAmelCase_      :					int     =			self.ce_loss(A_    ,    torch.arange(compare_scores.shape[1]).to(A_))
									lowerCAmelCase_      :					str     =			self.ce_loss(compare_scores.t()    ,    torch.arange(compare_scores.shape[0]).to(A_))
									lowerCAmelCase_      :					List[Any]     =			(loss_qa + loss_aq) / 2
									return loss





