



from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class      __lowerCAmelCase     :
					"""simple docstring"""






					A__				:		Dict			       =    PegasusConfig
					A__				:		List[Any]			       =    {}
					A__				:		int			       =    "gelu"



					def __init__(     self					:       Dict				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Dict=13				,					_snake_case					:       Union[str, Any]=7				,					_snake_case					:       List[str]=True				,					_snake_case					:       List[str]=False				,					_snake_case					:       Any=99				,					_snake_case					:       List[Any]=32				,					_snake_case					:       List[Any]=2				,					_snake_case					:       Tuple=4				,					_snake_case					:       Union[str, Any]=37				,					_snake_case					:       Optional[Any]=0.1				,					_snake_case					:       Tuple=0.1				,					_snake_case					:       Optional[int]=40				,					_snake_case					:       Optional[Any]=2				,					_snake_case					:       Tuple=1				,					_snake_case					:       int=0				,					):






								"""simple docstring"""


								A__       =    parent
								A__       =    batch_size
								A__       =    seq_length
								A__       =    is_training
								A__       =    use_labels
								A__       =    vocab_size
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    intermediate_size

								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    max_position_embeddings
								A__       =    eos_token_id
								A__       =    pad_token_id
								A__       =    bos_token_id



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    ids_tensor([self.batch_size, self.seq_length - 1]				,					self.vocab_size					)
								A__       =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)				,					1					)
								A__       =    tf.concat([input_ids, eos_tensor]				,					axis=1					)

								A__       =    ids_tensor([self.batch_size, self.seq_length]				,					self.vocab_size					)

								A__       =    self.config_cls(
								    vocab_size=self.vocab_size				,					d_model=self.hidden_size				,					encoder_layers=self.num_hidden_layers				,					decoder_layers=self.num_hidden_layers				,					encoder_attention_heads=self.num_attention_heads				,					decoder_attention_heads=self.num_attention_heads				,					encoder_ffn_dim=self.intermediate_size				,					decoder_ffn_dim=self.intermediate_size				,					dropout=self.hidden_dropout_prob				,					attention_dropout=self.attention_probs_dropout_prob				,					max_position_embeddings=self.max_position_embeddings				,					eos_token_ids=[2]				,					bos_token_id=self.bos_token_id				,					pad_token_id=self.pad_token_id				,					decoder_start_token_id=self.pad_token_id				,					**self.config_updates				,					)
								A__       =    prepare_pegasus_inputs_dict(_snake_case				,					_snake_case				,					_snake_case					)
								return config, inputs_dict



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       List[str]					):






								"""simple docstring"""


								A__       =    TFPegasusModel(config=_snake_case					).get_decoder()
								A__       =    inputs_dict['input_ids']

								A__       =    input_ids[:1, :]
								A__       =    inputs_dict['attention_mask'][:1, :]
								A__       =    inputs_dict['head_mask']
								A__       =    1

								# first forward pass
								A__       =    model(_snake_case				,					attention_mask=_snake_case				,					head_mask=_snake_case				,					use_cache=_snake_case					)

								A__     , A__       =    outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								A__       =    ids_tensor((self.batch_size, 3)				,					config.vocab_size					)
								A__       =    tf.cast(ids_tensor((self.batch_size, 3)				,					2					)				,					tf.inta					)

								# append to next input_ids and
								A__       =    tf.concat([input_ids, next_tokens]				,					axis=-1					)
								A__       =    tf.concat([attention_mask, next_attn_mask]				,					axis=-1					)

								A__       =    model(_snake_case				,					attention_mask=_snake_case					)[0]
								A__       =    model(_snake_case				,					attention_mask=_snake_case				,					past_key_values=_snake_case					)[0]

								self.parent.assertEqual(next_tokens.shape[1]				,					output_from_past.shape[1]					)

								# select random slice
								A__       =    int(ids_tensor((1,)				,					output_from_past.shape[-1]					)					)
								A__       =    output_from_no_past[:, -3:, random_slice_idx]
								A__       =    output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(_snake_case				,					_snake_case				,					rtol=1E-3					)







def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	)						->      Tuple:
			if attention_mask is None:
						A__       =    tf.cast(tf.math.not_equal(__UpperCamelCase ,	config.pad_token_id					) ,	tf.inta					)
			if decoder_attention_mask is None:
						A__       =    tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape ,	dtype=tf.inta					),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,	config.pad_token_id					) ,	tf.inta					),
						    ] ,	axis=-1 ,	)
			if head_mask is None:
						A__       =    tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
			if decoder_head_mask is None:
						A__       =    tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
			if cross_attn_head_mask is None:
						A__       =    tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}


@require_tf
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
					A__				:		Any			       =    (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
					A__				:		Optional[int]			       =    (
					    {
					        "conversational": TFPegasusForConditionalGeneration,
					        "feature-extraction": TFPegasusModel,
					        "summarization": TFPegasusForConditionalGeneration,
					        "text2text-generation": TFPegasusForConditionalGeneration,
					        "translation": TFPegasusForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					A__				:		Optional[Any]			       =    True
					A__				:		Optional[Any]			       =    False
					A__				:		Union[str, Any]			       =    False



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    TFPegasusModelTester(self					)
								A__       =    ConfigTester(self				,					config_class=_snake_case					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self.config_tester.run_common_tests()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*_snake_case					)







@require_sentencepiece
@require_tokenizers
@require_tf
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Optional[int]			       =    [
					    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
					    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
					]
					A__				:		Union[str, Any]			       =    [
					    "California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
					    " reduce the risk of wildfires.",
					    "N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
					]  # differs slightly from pytorch, likely due to numerical differences in linear layers
					A__				:		Optional[int]			       =    "google/pegasus-xsum"



					@cached_property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								return AutoTokenizer.from_pretrained(self.model_name					)



					@cached_property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
								return model



					def     _a							(     self					:       Union[str, Any]				,					**_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    self.translate_src_text(**_snake_case					)
								assert self.expected_text == generated_words



					def     _a							(     self					:       Any				,					**_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    self.tokenizer(self.src_text				,					**_snake_case				,					padding=_snake_case				,					return_tensors='tf'					)
								A__       =    self.model.generate(
								    model_inputs.input_ids				,					attention_mask=model_inputs.attention_mask				,					num_beams=2				,					use_cache=_snake_case				,					)
								A__       =    self.tokenizer.batch_decode(generated_ids.numpy()				,					skip_special_tokens=_snake_case					)
								return generated_words



					@slow
					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								self._assert_generated_batch_equal_expected()









import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
							import torch
							from torch import nn

							from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
							from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image


class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       List[Any]				,					_snake_case					:       Any				,					_snake_case					:       Optional[int]=13				,					_snake_case					:       Optional[Any]=64				,					_snake_case					:       List[str]=2				,					_snake_case					:       Any=3				,					_snake_case					:       Union[str, Any]=True				,					_snake_case					:       Dict=True				,					_snake_case					:       int=32				,					_snake_case					:       int=5				,					_snake_case					:       Union[str, Any]=4				,					_snake_case					:       int=37				,					_snake_case					:       Tuple="gelu"				,					_snake_case					:       Optional[int]=0.1				,					_snake_case					:       Dict=0.1				,					_snake_case					:       List[str]=10				,					_snake_case					:       Union[str, Any]=0.02				,					_snake_case					:       Dict=[1, 16, 4, 4]				,					_snake_case					:       Dict=None				,					):






								"""simple docstring"""


								A__       =    parent
								A__       =    batch_size
								A__       =    image_size
								A__       =    patch_size
								A__       =    num_channels
								A__       =    is_training
								A__       =    use_labels
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    intermediate_size
								A__       =    hidden_act
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    type_sequence_label_size
								A__       =    initializer_range
								A__       =    scope
								A__       =    backbone_featmap_shape

								# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								# the number of patches is based on the feature map of the backbone, which by default uses an output stride
								# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
								A__       =    (self.image_size // 32) ** 2
								A__       =    num_patches + 1



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

								A__       =    self.get_config()

								return config, pixel_values, labels



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    {
								    'global_padding': 'same',
								    'layer_type': 'bottleneck',
								    'depths': [3, 4, 9],
								    'out_features': ['stage1', 'stage2', 'stage3'],
								    'embedding_dynamic_padding': True,
								    'hidden_sizes': [4, 8, 16, 32],
								    'num_groups': 2,
								}

								return ViTHybridConfig(
								    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=_snake_case				,					initializer_range=self.initializer_range				,					backbone_featmap_shape=self.backbone_featmap_shape				,					backbone_config=_snake_case				,					)



					def     _a							(     self					:       int				,					_snake_case					:       Optional[int]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    ViTHybridModel(config=_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case					)
								self.parent.assertEqual(result.last_hidden_state.shape				,					(self.batch_size, self.seq_length, self.hidden_size)					)



					def     _a							(     self					:       List[str]				,					_snake_case					:       str				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    self.type_sequence_label_size
								A__       =    ViTHybridForImageClassification(_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case				,					labels=_snake_case					)
								self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.type_sequence_label_size)					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


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







@require_torch
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					A__				:		str			       =    (
					    {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					A__				:		Union[str, Any]			       =    False
					A__				:		Any			       =    False
					A__				:		Union[str, Any]			       =    False



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    ViTHybridModelTester(self					)
								A__       =    ConfigTester(self				,					config_class=_snake_case				,					has_text_modality=_snake_case				,					hidden_size=37					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self.config_tester.run_common_tests()



					@unittest.skip(reason='ViT does not use inputs_embeds'					)
					def     _a							(     self					:       int					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__       =    model_class(_snake_case					)
											self.assertIsInstance(model.get_input_embeddings()				,					(nn.Module)					)
											A__       =    model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_snake_case				,					nn.Linear					)					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

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

											A__       =    ['pixel_values']
											self.assertListEqual(arg_names[:1]				,					_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								A__       =    _config_zero_init(_snake_case					)
								for model_class in self.all_model_classes:
											A__       =    model_class(config=_snake_case					)
											# Skip the check for the backbone
											for name, module in model.named_modules():
														if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																	A__       =    [F'''{name}.{key}''' for key in module.state_dict().keys()]
																	break

											for name, param in model.named_parameters():
														if param.requires_grad:
																	if name in backbone_params:
																				continue
																	self.assertIn(
																	    ((param.data.mean() * 1E9).round() / 1E9).item()				,					[0.0, 1.0]				,					msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,					)



					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__       =    ViTHybridModel.from_pretrained(_snake_case					)
											self.assertIsNotNone(_snake_case					)







def A						(       )						->      Union[str, Any]:
			A__       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
			return image






@require_torch
@require_vision
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@cached_property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								return (
								    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
								    if is_vision_available()
								    else None
								)



					@slow
					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(
								    _snake_case					)

								A__       =    self.default_image_processor
								A__       =    prepare_img()
								A__       =    image_processor(images=_snake_case				,					return_tensors='pt'					).to(_snake_case					)

								# forward pass
								with torch.no_grad():
											A__       =    model(**_snake_case					)

								# verify the logits
								A__       =    torch.Size((1, 10_00)					)
								self.assertEqual(outputs.logits.shape				,					_snake_case					)

								A__       =    torch.tensor([-1.9090, -0.4993, -0.2389]					).to(_snake_case					)

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



					@slow
					@require_accelerate
					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'					)
								A__       =    ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'				,					device_map='auto'					)

								A__       =    prepare_img()

								A__       =    image_processor(images=_snake_case				,					return_tensors='pt'					)
								A__       =    model(**_snake_case					)
								A__       =    outputs.logits
								# model predicts one of the 1000 ImageNet classes
								A__       =    logits.argmax(-1					).item()

								self.assertTrue(model.config.idalabel[predicted_class_idx]				,					'tabby, tabby cat'					)




