"""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        snake_case :




					"""simple docstring"""

					def __init__(       self     :			Any					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Union[str, Any]=13					,lowerCamelCase__     :			str=30					,lowerCamelCase__     :			Any=2					,lowerCamelCase__     :			Dict=3					,lowerCamelCase__     :			Optional[Any]=True					,lowerCamelCase__     :			Any=True					,lowerCamelCase__     :			Union[str, Any]=32					,lowerCamelCase__     :			int=2					,lowerCamelCase__     :			List[str]=4					,lowerCamelCase__     :			Dict=37					,lowerCamelCase__     :			Any="gelu"					,lowerCamelCase__     :			Optional[int]=0.1					,lowerCamelCase__     :			List[str]=0.1					,lowerCamelCase__     :			List[str]=10					,lowerCamelCase__     :			Optional[int]=0.0_2					,lowerCamelCase__     :			Any=3					,lowerCamelCase__     :			Dict=None					,lowerCamelCase__     :			Union[str, Any]=2					,):
									UpperCAmelCase__    =							parent
									UpperCAmelCase__    =							batch_size
									UpperCAmelCase__    =							image_size
									UpperCAmelCase__    =							patch_size
									UpperCAmelCase__    =							num_channels
									UpperCAmelCase__    =							is_training
									UpperCAmelCase__    =							use_labels
									UpperCAmelCase__    =							hidden_size
									UpperCAmelCase__    =							num_hidden_layers
									UpperCAmelCase__    =							num_attention_heads
									UpperCAmelCase__    =							intermediate_size
									UpperCAmelCase__    =							hidden_act
									UpperCAmelCase__    =							hidden_dropout_prob
									UpperCAmelCase__    =							attention_probs_dropout_prob
									UpperCAmelCase__    =							type_sequence_label_size
									UpperCAmelCase__    =							initializer_range
									UpperCAmelCase__    =							scope
									UpperCAmelCase__    =							encoder_stride

									# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
									UpperCAmelCase__    =							(image_size // patch_size) ** 2
									UpperCAmelCase__    =							num_patches + 2







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

									UpperCAmelCase__    =							self.get_config()

									return config, pixel_values, labels







					def 	__lowerCAmelCase		(       self     :			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=__UpperCamelCase					,initializer_range=self.initializer_range					,encoder_stride=self.encoder_stride					,)







					def 	__lowerCAmelCase		(       self     :			List[str]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__    =							TFDeiTModel(config=__UpperCamelCase    )
									UpperCAmelCase__    =							model(__UpperCamelCase    )
									self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Any    ):
									UpperCAmelCase__    =							TFDeiTForMaskedImageModeling(config=__UpperCamelCase    )
									UpperCAmelCase__    =							model(__UpperCamelCase    )
									self.parent.assertEqual(
									    result.reconstruction.shape					,(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

									# test greyscale images
									UpperCAmelCase__    =							1
									UpperCAmelCase__    =							TFDeiTForMaskedImageModeling(__UpperCamelCase    )

									UpperCAmelCase__    =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
									UpperCAmelCase__    =							model(__UpperCamelCase    )
									self.parent.assertEqual(result.reconstruction.shape					,(self.batch_size, 1, self.image_size, self.image_size)    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Any    ):
									UpperCAmelCase__    =							self.type_sequence_label_size
									UpperCAmelCase__    =							TFDeiTForImageClassification(__UpperCamelCase    )
									UpperCAmelCase__    =							model(__UpperCamelCase					,labels=__UpperCamelCase    )
									self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)    )

									# test greyscale images
									UpperCAmelCase__    =							1
									UpperCAmelCase__    =							TFDeiTForImageClassification(__UpperCamelCase    )

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







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






@require_tf
class        snake_case (				__snake_case   ,					__snake_case   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				(
					    (
					        TFDeiTModel,
					        TFDeiTForImageClassification,
					        TFDeiTForImageClassificationWithTeacher,
					        TFDeiTForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					snake_case__       					=				(
					    {
					        "feature-extraction": TFDeiTModel,
					        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
					    }
					    if is_tf_available()
					    else {}
					)

					snake_case__       					=				False
					snake_case__       					=				False
					snake_case__       					=				False
					snake_case__       					=				False







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									UpperCAmelCase__    =							TFDeiTModelTester(self    )
									UpperCAmelCase__    =							ConfigTester(self					,config_class=__UpperCamelCase					,has_text_modality=__UpperCamelCase					,hidden_size=37    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									self.config_tester.run_common_tests()







					@unittest.skip(reason='DeiT does not use inputs_embeds'    )
					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									pass







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													UpperCAmelCase__    =							model_class(__UpperCamelCase    )
													self.assertIsInstance(model.get_input_embeddings()					,(tf.keras.layers.Layer)    )
													UpperCAmelCase__    =							model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__UpperCamelCase					,tf.keras.layers.Dense    )    )







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs_for_common()

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

													UpperCAmelCase__    =							['pixel_values']
													self.assertListEqual(arg_names[:1]					,__UpperCamelCase    )







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase    )







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase    )







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase    )







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			List[str]=False    ):
									UpperCAmelCase__    =							super()._prepare_for_class(__UpperCamelCase					,__UpperCamelCase					,return_labels=__UpperCamelCase    )

									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 	__lowerCAmelCase		(       self     :			Optional[int]    ):


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






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







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




					"""simple docstring"""

					@cached_property
					def 	__lowerCAmelCase		(       self     :			int    ):
									return (
									    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'    )
									    if is_vision_available()
									    else None
									)







					@slow
					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'    )

									UpperCAmelCase__    =							self.default_image_processor
									UpperCAmelCase__    =							prepare_img()
									UpperCAmelCase__    =							image_processor(images=__UpperCamelCase					,return_tensors='tf'    )

									# forward pass
									UpperCAmelCase__    =							model(**__UpperCamelCase    )

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

									UpperCAmelCase__    =							tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]    )

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




"""simple docstring"""






from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]







					@register_to_config
					def __init__(       self     :			str					,lowerCamelCase__     :			int					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			int = 50_257					,lowerCamelCase__     :			int = 1_024					,lowerCamelCase__     :			int = 768					,lowerCamelCase__     :			int = 12					,lowerCamelCase__     :			int = 12					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			str = "gelu_new"					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 1e-5					,lowerCamelCase__     :			float = 0.0_2					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = False					,lowerCamelCase__     :			bool = False					,):
									super().__init__()

									UpperCAmelCase__    =							prefix_length

									if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
													raise ValueError(
													    f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
													    f''' `n_embd`: {n_embd} are not equal.'''    )

									UpperCAmelCase__    =							prefix_inner_dim
									UpperCAmelCase__    =							prefix_hidden_dim

									UpperCAmelCase__    =							(
									    nn.Linear(self.prefix_inner_dim					,self.prefix_hidden_dim    )
									    if self.prefix_hidden_dim is not None
									    else nn.Identity()
									)
									UpperCAmelCase__    =							(
									    nn.Linear(self.prefix_hidden_dim					,lowerCamelCase__    ) if self.prefix_hidden_dim is not None else nn.Identity()
									)

									UpperCAmelCase__    =							GPTaConfig(
									    vocab_size=lowerCamelCase__					,n_positions=lowerCamelCase__					,n_embd=lowerCamelCase__					,n_layer=lowerCamelCase__					,n_head=lowerCamelCase__					,n_inner=lowerCamelCase__					,activation_function=lowerCamelCase__					,resid_pdrop=lowerCamelCase__					,embd_pdrop=lowerCamelCase__					,attn_pdrop=lowerCamelCase__					,layer_norm_epsilon=lowerCamelCase__					,initializer_range=lowerCamelCase__					,scale_attn_weights=lowerCamelCase__					,use_cache=lowerCamelCase__					,scale_attn_by_inverse_layer_idx=lowerCamelCase__					,reorder_and_upcast_attn=lowerCamelCase__					,)
									UpperCAmelCase__    =							GPTaLMHeadModel(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,):
									UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )
									UpperCAmelCase__    =							self.encode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							self.decode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							torch.cat((prefix_embeds, embedding_text)					,dim=1    )

									if labels is not None:
													UpperCAmelCase__    =							self.get_dummy_token(input_ids.shape[0]					,input_ids.device    )
													UpperCAmelCase__    =							torch.cat((dummy_token, input_ids)					,dim=1    )
									UpperCAmelCase__    =							self.transformer(inputs_embeds=lowerCamelCase__					,labels=lowerCamelCase__					,attention_mask=lowerCamelCase__    )
									if self.prefix_hidden_dim is not None:
													return out, hidden
									else:
													return out







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			torch.device    ):
									return torch.zeros(lowerCamelCase__					,self.prefix_length					,dtype=torch.intaa					,device=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[str]    ):
									return self.encode_prefix(lowerCamelCase__    )







					@torch.no_grad()
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__    =							torch.split(lowerCamelCase__					,1					,dim=0    )
									UpperCAmelCase__    =							[]
									UpperCAmelCase__    =							[]
									for feature in features:
													UpperCAmelCase__    =							self.decode_prefix(feature.to(lowerCamelCase__    )    )  # back to the clip feature
													# Only support beam search for now
													UpperCAmelCase__   ,			UpperCAmelCase__    =							self.generate_beam(
													    input_embeds=lowerCamelCase__					,device=lowerCamelCase__					,eos_token_id=lowerCamelCase__    )
													generated_tokens.append(output_tokens[0]    )
													generated_seq_lengths.append(seq_lengths[0]    )
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )
									return generated_tokens, generated_seq_lengths







					@torch.no_grad()
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			str=None					,lowerCamelCase__     :			List[Any]=None					,lowerCamelCase__     :			Tuple=None					,lowerCamelCase__     :			int = 5					,lowerCamelCase__     :			int = 67					,lowerCamelCase__     :			float = 1.0					,lowerCamelCase__     :			Optional[int] = None					,):
									UpperCAmelCase__    =							eos_token_id
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							torch.ones(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.int    )
									UpperCAmelCase__    =							torch.zeros(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.bool    )

									if input_embeds is not None:
													UpperCAmelCase__    =							input_embeds
									else:
													UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )

									for i in range(lowerCamelCase__    ):
													UpperCAmelCase__    =							self.transformer(inputs_embeds=lowerCamelCase__    )
													UpperCAmelCase__    =							outputs.logits
													UpperCAmelCase__    =							logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
													UpperCAmelCase__    =							logits.softmax(-1    ).log()

													if scores is None:
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							logits.topk(lowerCamelCase__					,-1    )
																	UpperCAmelCase__    =							generated.expand(lowerCamelCase__					,*generated.shape[1:]    )
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							next_tokens.permute(1					,0    ), scores.squeeze(0    )
																	if tokens is None:
																					UpperCAmelCase__    =							next_tokens
																	else:
																					UpperCAmelCase__    =							tokens.expand(lowerCamelCase__					,*tokens.shape[1:]    )
																					UpperCAmelCase__    =							torch.cat((tokens, next_tokens)					,dim=1    )
													else:
																	UpperCAmelCase__    =							-float(np.inf    )
																	UpperCAmelCase__    =							0
																	UpperCAmelCase__    =							scores[:, None] + logits
																	seq_lengths[~is_stopped] += 1
																	UpperCAmelCase__    =							scores_sum / seq_lengths[:, None]
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							scores_sum_average.view(-1    ).topk(lowerCamelCase__					,-1    )
																	UpperCAmelCase__    =							next_tokens // scores_sum.shape[1]
																	UpperCAmelCase__    =							seq_lengths[next_tokens_source]
																	UpperCAmelCase__    =							next_tokens % scores_sum.shape[1]
																	UpperCAmelCase__    =							next_tokens.unsqueeze(1    )
																	UpperCAmelCase__    =							tokens[next_tokens_source]
																	UpperCAmelCase__    =							torch.cat((tokens, next_tokens)					,dim=1    )
																	UpperCAmelCase__    =							generated[next_tokens_source]
																	UpperCAmelCase__    =							scores_sum_average * seq_lengths
																	UpperCAmelCase__    =							is_stopped[next_tokens_source]

													UpperCAmelCase__    =							self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]					,1					,-1    )
													UpperCAmelCase__    =							torch.cat((generated, next_token_embed)					,dim=1    )
													UpperCAmelCase__    =							is_stopped + next_tokens.eq(lowerCamelCase__    ).squeeze()
													if is_stopped.all():
																	break

									UpperCAmelCase__    =							scores / seq_lengths
									UpperCAmelCase__    =							scores.argsort(descending=lowerCamelCase__    )
									# tokens tensors are already padded to max_seq_length
									UpperCAmelCase__    =							[tokens[i] for i in order]
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__					,dim=0    )
									UpperCAmelCase__    =							torch.tensor([seq_lengths[i] for i in order]					,dtype=seq_lengths.dtype    )
									return output_texts, seq_lengths



