
"""simple docstring"""


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor




class  __lowerCamelCase  :




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=3							,						UpperCAmelCase=32							,						UpperCAmelCase=3							,						UpperCAmelCase=10							,						UpperCAmelCase=[10, 20, 30, 40]							,						UpperCAmelCase=[1, 1, 2, 1]							,						UpperCAmelCase=True							,						UpperCAmelCase=True							,						UpperCAmelCase="relu"							,						UpperCAmelCase=3							,						UpperCAmelCase=None							,						):
							lowerCamelCase_					=  parent
							lowerCamelCase_					=  batch_size
							lowerCamelCase_					=  image_size
							lowerCamelCase_					=  num_channels
							lowerCamelCase_					=  embeddings_size
							lowerCamelCase_					=  hidden_sizes
							lowerCamelCase_					=  depths
							lowerCamelCase_					=  is_training
							lowerCamelCase_					=  use_labels
							lowerCamelCase_					=  hidden_act
							lowerCamelCase_					=  num_labels
							lowerCamelCase_					=  scope
							lowerCamelCase_					=  len(UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							lowerCamelCase_					=  None
							if self.use_labels:
										lowerCamelCase_					=  ids_tensor([self.batch_size]							,						self.num_labels   )

							lowerCamelCase_					=  self.get_config()
							return config, pixel_values, labels




				def        UpperCAmelCase__						(				self   ):
							return RegNetConfig(
							    num_channels=self.num_channels							,						embeddings_size=self.embeddings_size							,						hidden_sizes=self.hidden_sizes							,						depths=self.depths							,						hidden_act=self.hidden_act							,						num_labels=self.num_labels							,						)




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  TFRegNetModel(config=UpperCAmelCase   )
							lowerCamelCase_					=  model(UpperCAmelCase							,						training=UpperCAmelCase   )
							# expected last hidden states: B, C, H // 32, W // 32
							self.parent.assertEqual(
							    result.last_hidden_state.shape							,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,						)




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  self.num_labels
							lowerCamelCase_					=  TFRegNetForImageClassification(UpperCAmelCase   )
							lowerCamelCase_					=  model(UpperCAmelCase							,						labels=UpperCAmelCase							,						training=UpperCAmelCase   )
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels)   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.prepare_config_and_inputs()
							lowerCamelCase_					=  config_and_inputs
							lowerCamelCase_					=  {'pixel_values': pixel_values}
							return config, inputs_dict




@require_tf
class  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase						,	unittest.TestCase			):
				a__:      str          =    (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
				a__:      List[str]          =    (
				    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
				    if is_tf_available()
				    else {}
				)

				a__:      str          =    False
				a__:      Optional[Any]          =    False
				a__:      List[str]          =    False
				a__:      Optional[int]          =    False
				a__:      str          =    False




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  TFRegNetModelTester(self   )
							lowerCamelCase_					=  ConfigTester(self							,						config_class=UpperCAmelCase							,						has_text_modality=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							return




				@unittest.skip(reason='''RegNet does not use inputs_embeds'''   )
				def        UpperCAmelCase__						(				self   ):
							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.'''							,						)
				@slow
				def        UpperCAmelCase__						(				self   ):
							super().test_keras_fit()




				@unittest.skip(reason='''RegNet does not support input and output embeddings'''   )
				def        UpperCAmelCase__						(				self   ):
							pass




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCamelCase_					=  ['pixel_values']
										self.assertListEqual(arg_names[:1]							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							def check_hidden_states_output(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
										lowerCamelCase_					=  model_class(UpperCAmelCase   )
										lowerCamelCase_					=  model(**self._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase   )							,						training=UpperCAmelCase   )

										lowerCamelCase_					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										lowerCamelCase_					=  self.model_tester.num_stages
										self.assertEqual(len(UpperCAmelCase   )							,						expected_num_stages + 1   )

										# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]   )							,						[self.model_tester.image_size // 2, self.model_tester.image_size // 2]							,						)

							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs_for_common()
							lowerCamelCase_					=  ['basic', 'bottleneck']
							for model_class in self.all_model_classes:
										for layer_type in layers_type:
													lowerCamelCase_					=  layer_type
													lowerCamelCase_					=  True
													check_hidden_states_output(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )

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

													check_hidden_states_output(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs_for_common()

							def check_equivalence(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase={}   ):
										lowerCamelCase_					=  model(UpperCAmelCase							,						return_dict=UpperCAmelCase							,						**UpperCAmelCase   )
										lowerCamelCase_					=  model(UpperCAmelCase							,						return_dict=UpperCAmelCase							,						**UpperCAmelCase   ).to_tuple()

										def recursive_check(UpperCAmelCase							,						UpperCAmelCase   ):
													if isinstance(UpperCAmelCase							,						(List, Tuple)   ):
																for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase							,						UpperCAmelCase   ):
																			recursive_check(UpperCAmelCase							,						UpperCAmelCase   )
													elif tuple_object is None:
																return
													else:
																self.assertTrue(
																    all(tf.equal(UpperCAmelCase							,						UpperCAmelCase   )   )							,						msg=(
																        '''Tuple and dict output are not equal. Difference:'''
																        f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object   )   )}"
																    )							,						)

													recursive_check(UpperCAmelCase							,						UpperCAmelCase   )

							for model_class in self.all_model_classes:
										lowerCamelCase_					=  model_class(UpperCAmelCase   )

										lowerCamelCase_					=  self._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase   )
										lowerCamelCase_					=  self._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase   )
										check_equivalence(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )

										lowerCamelCase_					=  self._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase							,						return_labels=UpperCAmelCase   )
										lowerCamelCase_					=  self._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase							,						return_labels=UpperCAmelCase   )
										check_equivalence(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )

										lowerCamelCase_					=  self._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase   )
										lowerCamelCase_					=  self._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase   )
										check_equivalence(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						{'''output_hidden_states''': True}   )

										lowerCamelCase_					=  self._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase							,						return_labels=UpperCAmelCase   )
										lowerCamelCase_					=  self._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase							,						return_labels=UpperCAmelCase   )
										check_equivalence(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						{'''output_hidden_states''': True}   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase   )





				@slow
				def        UpperCAmelCase__						(				self   ):
							for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase_					=  TFRegNetModel.from_pretrained(UpperCAmelCase   )
										self.assertIsNotNone(UpperCAmelCase   )






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




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




				@cached_property
				def        UpperCAmelCase__						(				self   ):
							return (
							    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
							    if is_vision_available()
							    else None
							)





				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

							lowerCamelCase_					=  self.default_image_processor
							lowerCamelCase_					=  prepare_img()
							lowerCamelCase_					=  image_processor(images=UpperCAmelCase							,						return_tensors='''tf'''   )

							# forward pass
							lowerCamelCase_					=  model(**UpperCAmelCase							,						training=UpperCAmelCase   )

							# verify the logits
							lowerCamelCase_					=  tf.TensorShape((1, 1000)   )
							self.assertEqual(outputs.logits.shape							,						UpperCAmelCase   )

							lowerCamelCase_					=  tf.constant([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]   )

							tf.debugging.assert_near(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 lowerCAmelCase__      (	lowercase,      lowercase,      lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     [R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]
			@register_to_config
			def __init__(    self    , lowercase    , lowercase    , lowercase = None    , lowercase = 50257    , lowercase = 1024    , lowercase = 768    , lowercase = 12    , lowercase = 12    , lowercase = None    , lowercase = "gelu_new"    , lowercase = 0.1    , lowercase = 0.1    , lowercase = 0.1    , lowercase = 1E-5    , lowercase = 0.02    , lowercase = True    , lowercase = True    , lowercase = False    , lowercase = False    , ):
							super().__init__()

							_lowerCamelCase	:     Tuple       =       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.'''    )

							_lowerCamelCase	:     Optional[Any]       =       prefix_inner_dim
							_lowerCamelCase	:     Tuple       =       prefix_hidden_dim

							_lowerCamelCase	:     Dict       =       (
							    nn.Linear(self.prefix_inner_dim    , self.prefix_hidden_dim    )
							    if self.prefix_hidden_dim is not None
							    else nn.Identity()
							)
							_lowerCamelCase	:     Tuple       =       (
							    nn.Linear(self.prefix_hidden_dim    , lowercase    ) if self.prefix_hidden_dim is not None else nn.Identity()
							)

							_lowerCamelCase	:     Any       =       GPTaConfig(
							    vocab_size=lowercase    , n_positions=lowercase    , n_embd=lowercase    , n_layer=lowercase    , n_head=lowercase    , n_inner=lowercase    , activation_function=lowercase    , resid_pdrop=lowercase    , embd_pdrop=lowercase    , attn_pdrop=lowercase    , layer_norm_epsilon=lowercase    , initializer_range=lowercase    , scale_attn_weights=lowercase    , use_cache=lowercase    , scale_attn_by_inverse_layer_idx=lowercase    , reorder_and_upcast_attn=lowercase    , )
							_lowerCamelCase	:     Dict       =       GPTaLMHeadModel(lowercase    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase = None    , lowercase = None    , ):
							_lowerCamelCase	:     List[Any]       =       self.transformer.transformer.wte(lowercase    )
							_lowerCamelCase	:     str       =       self.encode_prefix(lowercase    )
							_lowerCamelCase	:     int       =       self.decode_prefix(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       torch.cat((prefix_embeds, embedding_text)    , dim=1    )

							if labels is not None:
											_lowerCamelCase	:     Any       =       self.get_dummy_token(input_ids.shape[0]    , input_ids.device    )
											_lowerCamelCase	:     int       =       torch.cat((dummy_token, input_ids)    , dim=1    )
							_lowerCamelCase	:     List[Any]       =       self.transformer(inputs_embeds=lowercase    , labels=lowercase    , attention_mask=lowercase    )
							if self.prefix_hidden_dim is not None:
											return out, hidden
							else:
											return out
			def    A_       (    self    , lowercase    , lowercase    ):
							return torch.zeros(lowercase    , self.prefix_length    , dtype=torch.intaa    , device=lowercase    )
			def    A_       (    self    , lowercase    ):
							return self.encode_prefix(lowercase    )
			@torch.no_grad()
			def    A_       (    self    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Dict       =       torch.split(lowercase    , 1    , dim=0    )
							_lowerCamelCase	:     Any       =       []
							_lowerCamelCase	:     str       =       []
							for feature in features:
											_lowerCamelCase	:     Any       =       self.decode_prefix(feature.to(lowercase    )    )  # back to the clip feature
											# Only support beam search for now
											_lowerCamelCase,					_lowerCamelCase	:     Dict       =       self.generate_beam(
											    input_embeds=lowercase    , device=lowercase    , eos_token_id=lowercase    )
											generated_tokens.append(output_tokens[0]    )
											generated_seq_lengths.append(seq_lengths[0]    )
							_lowerCamelCase	:     List[Any]       =       torch.stack(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       torch.stack(lowercase    )
							return generated_tokens, generated_seq_lengths






			@torch.no_grad()
			def    A_       (    self    , lowercase=None    , lowercase=None    , lowercase=None    , lowercase = 5    , lowercase = 67    , lowercase = 1.0    , lowercase = None    , ):
							_lowerCamelCase	:     Any       =       eos_token_id
							_lowerCamelCase	:     List[str]       =       None
							_lowerCamelCase	:     List[Any]       =       None
							_lowerCamelCase	:     List[str]       =       torch.ones(lowercase    , device=lowercase    , dtype=torch.int    )
							_lowerCamelCase	:     Optional[int]       =       torch.zeros(lowercase    , device=lowercase    , dtype=torch.bool    )

							if input_embeds is not None:
											_lowerCamelCase	:     Optional[int]       =       input_embeds
							else:
											_lowerCamelCase	:     Union[str, Any]       =       self.transformer.transformer.wte(lowercase    )

							for i in range(lowercase    ):
											_lowerCamelCase	:     Any       =       self.transformer(inputs_embeds=lowercase    )
											_lowerCamelCase	:     Optional[Any]       =       outputs.logits
											_lowerCamelCase	:     List[Any]       =       logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
											_lowerCamelCase	:     Optional[int]       =       logits.softmax(-1    ).log()

											if scores is None:
															_lowerCamelCase,					_lowerCamelCase	:     Union[str, Any]       =       logits.topk(lowercase    , -1    )
															_lowerCamelCase	:     Optional[int]       =       generated.expand(lowercase    , *generated.shape[1:]    )
															_lowerCamelCase,					_lowerCamelCase	:     Tuple       =       next_tokens.permute(1    , 0    ), scores.squeeze(0    )
															if tokens is None:
																			_lowerCamelCase	:     List[str]       =       next_tokens
															else:
																			_lowerCamelCase	:     Tuple       =       tokens.expand(lowercase    , *tokens.shape[1:]    )
																			_lowerCamelCase	:     int       =       torch.cat((tokens, next_tokens)    , dim=1    )
											else:
															_lowerCamelCase	:     Union[str, Any]       =       -float(np.inf    )
															_lowerCamelCase	:     Union[str, Any]       =       0
															_lowerCamelCase	:     List[Any]       =       scores[:, None] + logits
															seq_lengths[~is_stopped] += 1
															_lowerCamelCase	:     List[Any]       =       scores_sum / seq_lengths[:, None]
															_lowerCamelCase,					_lowerCamelCase	:     Optional[Any]       =       scores_sum_average.view(-1    ).topk(lowercase    , -1    )
															_lowerCamelCase	:     Union[str, Any]       =       next_tokens // scores_sum.shape[1]
															_lowerCamelCase	:     Optional[Any]       =       seq_lengths[next_tokens_source]
															_lowerCamelCase	:     Dict       =       next_tokens % scores_sum.shape[1]
															_lowerCamelCase	:     Optional[int]       =       next_tokens.unsqueeze(1    )
															_lowerCamelCase	:     Tuple       =       tokens[next_tokens_source]
															_lowerCamelCase	:     str       =       torch.cat((tokens, next_tokens)    , dim=1    )
															_lowerCamelCase	:     Optional[int]       =       generated[next_tokens_source]
															_lowerCamelCase	:     Dict       =       scores_sum_average * seq_lengths
															_lowerCamelCase	:     List[Any]       =       is_stopped[next_tokens_source]

											_lowerCamelCase	:     Union[str, Any]       =       self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]    , 1    , -1    )
											_lowerCamelCase	:     Union[str, Any]       =       torch.cat((generated, next_token_embed)    , dim=1    )
											_lowerCamelCase	:     Optional[Any]       =       is_stopped + next_tokens.eq(lowercase    ).squeeze()
											if is_stopped.all():
															break

							_lowerCamelCase	:     Optional[int]       =       scores / seq_lengths
							_lowerCamelCase	:     Optional[int]       =       scores.argsort(descending=lowercase    )
							# tokens tensors are already padded to max_seq_length
							_lowerCamelCase	:     List[Any]       =       [tokens[i] for i in order]
							_lowerCamelCase	:     Dict       =       torch.stack(lowercase    , dim=0    )
							_lowerCamelCase	:     Optional[int]       =       torch.tensor([seq_lengths[i] for i in order]    , dtype=seq_lengths.dtype    )
							return output_texts, seq_lengths