





'''simple docstring'''






import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
		import torch
		from torch import nn

		from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor







class       lowerCamelCase       :


					def __init__(       self						:	Optional[int]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	str=False							,  __snake_case						:	List[Any]=13							,  __snake_case						:	str=(32, 1_28)							,  __snake_case						:	str=4							,  __snake_case						:	int=3							,  __snake_case						:	Union[str, Any]=27							,  __snake_case						:	Tuple=38							,  __snake_case						:	Any=99							,  __snake_case						:	Any=99							,  __snake_case						:	List[str]=32							,  __snake_case						:	int=5							,  __snake_case						:	Union[str, Any]=4							,  __snake_case						:	str=4.0							,  __snake_case						:	Optional[Any]=2_57							,  __snake_case						:	Union[str, Any]=None							,  ):





											'''simple docstring'''





											_snake_case: List[str]	       =		parent
											_snake_case: int	       =		is_training
											_snake_case: List[Any]	       =		batch_size
											_snake_case: Union[str, Any]	       =		image_size
											_snake_case: Any	       =		patch_size
											_snake_case: int	       =		num_channels
											_snake_case: str	       =		max_token_length
											_snake_case: Any	       =		num_character_labels
											_snake_case: List[str]	       =		num_bpe_labels
											_snake_case: Dict	       =		num_wordpiece_labels
											_snake_case: int	       =		hidden_size
											_snake_case: Tuple	       =		num_hidden_layers
											_snake_case: Optional[Any]	       =		num_attention_heads
											_snake_case: Union[str, Any]	       =		mlp_ratio
											_snake_case: int	       =		patch_embeds_hidden_size
											_snake_case: int	       =		output_hidden_states


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											_snake_case: List[str]	       =		floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)
											_snake_case: Any	       =		self.get_config()
											return config, pixel_values


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											return MgpstrConfig(
											    image_size=self.image_size							,  patch_size=self.patch_size							,  num_channels=self.num_channels							,  max_token_length=self.max_token_length							,  num_character_labels=self.num_character_labels							,  num_bpe_labels=self.num_bpe_labels							,  num_wordpiece_labels=self.num_wordpiece_labels							,  hidden_size=self.hidden_size							,  num_hidden_layers=self.num_hidden_layers							,  num_attention_heads=self.num_attention_heads							,  mlp_ratio=self.mlp_ratio							,  output_hidden_states=self.output_hidden_states							,  )


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	List[str]							,  __snake_case						:	Dict							):





											'''simple docstring'''





											_snake_case: List[str]	       =		MgpstrForSceneTextRecognition(__snake_case							)
											model.to(__snake_case							)
											model.eval()
											with torch.no_grad():
																	_snake_case: Tuple	       =		model(__snake_case							)
											self.parent.assertEqual(
											    generated_ids[0][0].shape							,  (self.batch_size, self.max_token_length, self.num_character_labels)							)




					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		self.prepare_config_and_inputs()
											_snake_case       ,    _snake_case: Any	       =		config_and_inputs
											_snake_case: str	       =		{'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
class       lowerCamelCase       (    __UpperCAmelCase  ,				__UpperCAmelCase  ,				unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
					_SCREAMING_SNAKE_CASE				 =     {"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
					_SCREAMING_SNAKE_CASE				 =     False

					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: str	       =		MgpstrModelTester(self							)
											_snake_case: Union[str, Any]	       =		ConfigTester(self							,  config_class=__snake_case							,  has_text_modality=__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											self.config_tester.run_common_tests()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: str	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case							)


					@unittest.skip(reason='MgpstrModel does not use inputs_embeds'							)
					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											pass


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





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

											for model_class in self.all_model_classes:
																	_snake_case: Dict	       =		model_class(__snake_case							)
																	self.assertIsInstance(model.get_input_embeddings()							,  (nn.Module)							)
																	_snake_case: List[str]	       =		model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(__snake_case							,  nn.Linear							)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





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

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

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


					@unittest.skip(reason='MgpstrModel does not support feedforward chunking'							)
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											pass


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[Any]	       =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	if not model_class.supports_gradient_checkpointing:
																							continue

																	_snake_case: Any	       =		True
																	_snake_case: Tuple	       =		model_class(__snake_case							)
																	self.assertTrue(model.is_gradient_checkpointing							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											def check_hidden_states_output(__snake_case						:	Optional[int]							,  __snake_case						:	int							,  __snake_case						:	Tuple							):
																	_snake_case: Any	       =		model_class(__snake_case							)
																	model.to(__snake_case							)
																	model.eval()

																	with torch.no_grad():
																							_snake_case: Tuple	       =		model(**self._prepare_for_class(__snake_case							,  __snake_case							)							)

																	_snake_case: Union[str, Any]	       =		outputs.hidden_states

																	_snake_case: Union[str, Any]	       =		getattr(
																	    self.model_tester							,  'expected_num_hidden_layers'							,  self.model_tester.num_hidden_layers + 1							)
																	self.assertEqual(len(__snake_case							)							,  __snake_case							)

																	self.assertListEqual(
																	    list(hidden_states[0].shape[-2:]							)							,  [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]							,  )

											_snake_case       ,    _snake_case: Optional[Any]	       =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	_snake_case: 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"]
																	_snake_case: List[Any]	       =		True

																	check_hidden_states_output(__snake_case							,  __snake_case							,  __snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





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

											_snake_case: Optional[int]	       =		_config_zero_init(__snake_case							)
											for model_class in self.all_model_classes:
																	_snake_case: Tuple	       =		model_class(config=__snake_case							)
																	for name, param in model.named_parameters():
																							if isinstance(__snake_case							,  (nn.Linear, nn.Convad, nn.LayerNorm)							):
																													if param.requires_grad:
																																			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'''							,  )




					@unittest.skip(reason='Retain_grad is tested in individual model tests'							)
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											pass





def    lowercase_   (		) ->Optional[int]:
						_snake_case: List[Any]	       =		'https://i.postimg.cc/ZKwLg2Gw/367-14.png'
						_snake_case: Tuple	       =		Image.open(requests.get(lowercase__					,	stream=lowercase__    ).raw    ).convert('RGB'    )
						return im







@require_vision
@require_torch
class       lowerCamelCase       (    unittest.TestCase	):


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		'alibaba-damo/mgp-str-base'
											_snake_case: Dict	       =		MgpstrForSceneTextRecognition.from_pretrained(__snake_case							).to(__snake_case							)
											_snake_case: Dict	       =		MgpstrProcessor.from_pretrained(__snake_case							)

											_snake_case: Any	       =		prepare_img()
											_snake_case: int	       =		processor(images=__snake_case							,  return_tensors='pt'							).pixel_values.to(__snake_case							)

											# forward pass
											with torch.no_grad():
																	_snake_case: Dict	       =		model(__snake_case							)

											# verify the logits
											self.assertEqual(outputs.logits[0].shape							,  torch.Size((1, 27, 38)							)							)

											_snake_case: int	       =		processor.batch_decode(outputs.logits							)
											_snake_case: List[Any]	       =		'ticket'

											self.assertEqual(out_strs['generated_text'][0]							,  __snake_case							)

											_snake_case: Optional[Any]	       =		torch.tensor(
											    [[[-39.5_397, -44.4_024, -36.1_844], [-61.4_709, -63.8_639, -58.3_454], [-74.0_225, -68.5_494, -71.2_164]]]							,  device=__snake_case							,  )

											self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]							,  __snake_case							,  atol=1e-4							)							)












'''simple docstring'''






from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import (
		    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_MASKED_LM_MAPPING,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
		    TF_MODEL_FOR_PRETRAINING_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    TFSharedEmbeddings,
		)

		if _tf_gpu_memory_limit is not None:
				A :			Tuple							   =      tf.config.list_physical_devices('GPU')
				for gpu in gpus:
						# Restrict TensorFlow to only allocate x GB of memory on the GPUs
						try:
								tf.config.set_logical_device_configuration(
								    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
								)
								A :			Tuple							   =      tf.config.list_logical_devices('GPU')
								print('Logical GPUs', logical_gpus)
						except RuntimeError as e:
								# Virtual devices must be set before GPUs have been initialized
								print(e)







@require_tf
class       lowerCamelCase       :
					_SCREAMING_SNAKE_CASE				 =     None
					_SCREAMING_SNAKE_CASE				 =     ()
					_SCREAMING_SNAKE_CASE				 =     ()
					_SCREAMING_SNAKE_CASE				 =     True
					_SCREAMING_SNAKE_CASE				 =     True
					_SCREAMING_SNAKE_CASE				 =     True
					_SCREAMING_SNAKE_CASE				 =     False


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	int							,  __snake_case						:	Tuple=False							):





											'''simple docstring'''





											_snake_case: Tuple	       =		copy.deepcopy(__snake_case							)

											if model_class in get_values(__snake_case							):
																	_snake_case: Dict	       =		{
																	    k: tf.tile(tf.expand_dims(__snake_case							,  1							)							,  (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
																	    if isinstance(__snake_case							,  tf.Tensor							) and v.ndim > 0
																	    else v
																	    for k, v in inputs_dict.items()
																	}

											if return_labels:
																	if model_class in get_values(__snake_case							):
																							_snake_case: Any	       =		tf.ones(self.model_tester.batch_size							,  dtype=tf.intaa							)
																	elif model_class in get_values(__snake_case							):
																							_snake_case: Dict	       =		tf.zeros(self.model_tester.batch_size							,  dtype=tf.intaa							)
																							_snake_case: Optional[Any]	       =		tf.zeros(self.model_tester.batch_size							,  dtype=tf.intaa							)
																	elif model_class in [
																	    *get_values(__snake_case							),
																	    *get_values(__snake_case							),
																	]:
																							_snake_case: int	       =		tf.zeros(self.model_tester.batch_size							,  dtype=tf.intaa							)
																	elif model_class in get_values(__snake_case							):
																							_snake_case: Optional[int]	       =		tf.zeros(self.model_tester.batch_size							,  dtype=tf.intaa							)
																	elif model_class in [
																	    *get_values(__snake_case							),
																	    *get_values(__snake_case							),
																	    *get_values(__snake_case							),
																	    *get_values(__snake_case							),
																	    *get_values(__snake_case							),
																	]:
																							_snake_case: Union[str, Any]	       =		tf.zeros(
																							    (self.model_tester.batch_size, self.model_tester.seq_length)							,  dtype=tf.intaa							)
											return inputs_dict


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[int]	       =		self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes[:2]:
																	_snake_case: Tuple	       =		self._prepare_for_class(__snake_case							,  __snake_case							)
																	_snake_case: Dict	       =		model_class(__snake_case							)

																	@tf.function
																	def run_in_graph_mode():
																							return model(__snake_case							)

																	_snake_case: Dict	       =		run_in_graph_mode()
																	self.assertIsNotNone(__snake_case							)


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





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[int]	       =		self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes[:2]:
																	_snake_case: Optional[Any]	       =		self._prepare_for_class(__snake_case							,  __snake_case							)
																	_snake_case: str	       =		model_class(__snake_case							)

																	@tf.function(experimental_compile=__snake_case							)
																	def run_in_graph_mode():
																							return model(__snake_case							)

																	_snake_case: str	       =		run_in_graph_mode()
																	self.assertIsNotNone(__snake_case							)


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





											'''simple docstring'''





											_snake_case       ,    _snake_case: List[Any]	       =		self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes[:2]:
																	_snake_case: Dict	       =		model_class(__snake_case							)
																	if getattr(__snake_case							,  'hf_compute_loss'							,  __snake_case							):
																							# Test that model correctly compute the loss with kwargs
																							_snake_case: Tuple	       =		self._prepare_for_class(inputs_dict.copy()							,  __snake_case							,  return_labels=__snake_case							)
																							# Is there a better way to remove these decoder inputs?
																							_snake_case: Union[str, Any]	       =		{
																							    key: val
																							    for key, val in prepared_for_class.items()
																							    if key not in ('head_mask', 'decoder_head_mask', 'cross_attn_head_mask', 'decoder_input_ids')
																							}

																							_snake_case: List[str]	       =		{
																							    'labels',
																							    'label',
																							    'label_ids',
																							    'start_positions',
																							    'start_position',
																							    'end_positions',
																							    'end_position',
																							    'next_sentence_label',
																							}
																							_snake_case: Tuple	       =		possible_label_cols.intersection(set(__snake_case							)							)
																							self.assertGreater(len(__snake_case							)							,  0							,  msg='No matching label names found!'							)
																							_snake_case: Optional[Any]	       =		{key: val for key, val in prepared_for_class.items() if key in label_names}
																							_snake_case: Union[str, Any]	       =		{key: val for key, val in prepared_for_class.items() if key not in label_names}
																							self.assertGreater(len(__snake_case							)							,  0							)

																							# Make sure it works with XLA!
																							model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)							,  jit_compile=__snake_case							)
																							# Make sure the model fits without crashing regardless of where we pass the labels
																							_snake_case: Optional[Any]	       =		model.fit(
																							    __snake_case							,  validation_data=__snake_case							,  steps_per_epoch=1							,  validation_steps=1							,  shuffle=__snake_case							,  verbose=0							,  )
																							_snake_case: str	       =		history.history['loss'][0]
																							self.assertTrue(not isnan(__snake_case							)							)
																							_snake_case: Tuple	       =		history.history['val_loss'][0]
																							self.assertTrue(not isnan(__snake_case							)							)

																							# Now test it with separate labels, to make sure that path works in XLA too.
																							_snake_case: Any	       =		model_class(__snake_case							)
																							model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)							,  jit_compile=__snake_case							)
																							_snake_case: str	       =		model.fit(
																							    __snake_case							,  __snake_case							,  validation_data=(inputs_minus_labels, labels)							,  steps_per_epoch=1							,  validation_steps=1							,  shuffle=__snake_case							,  verbose=0							,  )

																							_snake_case: List[str]	       =		history.history['loss'][0]
																							self.assertTrue(not isnan(__snake_case							)							)
																							_snake_case: Optional[Any]	       =		history.history['val_loss'][0]
																							self.assertTrue(not isnan(__snake_case							)							)


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: List[Any]	       =		self.model_tester.prepare_config_and_inputs_for_common()
											_snake_case: int	       =		True
											_snake_case: int	       =		True

											if hasattr(__snake_case							,  'use_cache'							):
																	_snake_case: Optional[Any]	       =		True

											_snake_case: Union[str, Any]	       =		getattr(self.model_tester							,  'encoder_seq_length'							,  self.model_tester.seq_length							)
											_snake_case: int	       =		getattr(self.model_tester							,  'key_length'							,  __snake_case							)

											for model_class in self.all_model_classes[:2]:
																	_snake_case: Tuple	       =		self._prepare_for_class(__snake_case							,  __snake_case							)
																	_snake_case: int	       =		model_class(__snake_case							)
																	model.build()
																	_snake_case: str	       =		len(model(__snake_case							)							)

																	for key in list(class_inputs_dict.keys()							):
																							# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
																							if key not in model.input_signature:
																													del class_inputs_dict[key]
																							# Check it's a tensor, in case the inputs dict has some bools in it too
																							elif isinstance(class_inputs_dict[key]							,  tf.Tensor							) and class_inputs_dict[key].dtype.is_integer:
																													_snake_case: Dict	       =		tf.cast(class_inputs_dict[key]							,  tf.intaa							)

																	if set(class_inputs_dict.keys()							) != set(model.input_signature.keys()							):
																							continue  # Some models have inputs that the preparation functions don't create, we skip those

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							model.save_pretrained(__snake_case							,  saved_model=__snake_case							)
																							_snake_case: int	       =		os.path.join(__snake_case							,  'saved_model'							,  '1'							)
																							_snake_case: Optional[Any]	       =		tf.keras.models.load_model(__snake_case							)
																							_snake_case: Any	       =		model(__snake_case							)

																							if self.is_encoder_decoder:
																													_snake_case: str	       =		outputs['encoder_hidden_states']
																													_snake_case: Union[str, Any]	       =		outputs['encoder_attentions']
																							else:
																													_snake_case: Optional[int]	       =		outputs['hidden_states']
																													_snake_case: Dict	       =		outputs['attentions']

																							self.assertEqual(len(__snake_case							)							,  __snake_case							)

																							_snake_case: int	       =		getattr(
																							    self.model_tester							,  'expected_num_hidden_layers'							,  self.model_tester.num_hidden_layers + 1							)

																							self.assertEqual(len(__snake_case							)							,  __snake_case							)
																							self.assertListEqual(
																							    list(output_hidden_states[0].shape[-2:]							)							,  [self.model_tester.seq_length, self.model_tester.hidden_size]							,  )

																							self.assertEqual(len(__snake_case							)							,  self.model_tester.num_hidden_layers							)
																							self.assertListEqual(
																							    list(output_attentions[0].shape[-3:]							)							,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]							,  )


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											tf.keras.mixed_precision.set_global_policy('mixed_float16'							)

											# try/finally block to ensure subsequent tests run in float32
											try:
																	_snake_case       ,    _snake_case: List[str]	       =		self.model_tester.prepare_config_and_inputs_for_common()
																	for model_class in self.all_model_classes[:2]:
																							_snake_case: Dict	       =		self._prepare_for_class(__snake_case							,  __snake_case							)
																							_snake_case: Any	       =		model_class(__snake_case							)
																							_snake_case: List[str]	       =		model(__snake_case							)

																							self.assertIsNotNone(__snake_case							)
											finally:
																	tf.keras.mixed_precision.set_global_policy('float32'							)


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[int]	       =		self.model_tester.prepare_config_and_inputs_for_common()
											# head_mask and decoder_head_mask has different shapes than other input args
											if "head_mask" in inputs_dict:
																	del inputs_dict["head_mask"]
											if "decoder_head_mask" in inputs_dict:
																	del inputs_dict["decoder_head_mask"]
											if "cross_attn_head_mask" in inputs_dict:
																	del inputs_dict["cross_attn_head_mask"]
											_snake_case: List[str]	       =		{
											    module_member
											    for model_class in self.all_model_classes
											    for module in (import_module(model_class.__module__							),)
											    for module_member_name in dir(__snake_case							)
											    if module_member_name.endswith('MainLayer'							)
											    for module_member in (getattr(__snake_case							,  __snake_case							),)
											    if isinstance(__snake_case							,  __snake_case							)
											    and tf.keras.layers.Layer in module_member.__bases__
											    and getattr(__snake_case							,  '_keras_serializable'							,  __snake_case							)
											}

											for main_layer_class in tf_main_layer_classes:
																	# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
																	if "T5" in main_layer_class.__name__:
																							# Take the same values than in TFT5ModelTester for this shared layer
																							_snake_case: Optional[Any]	       =		TFSharedEmbeddings(self.model_tester.vocab_size							,  self.model_tester.hidden_size							,  name='shared'							)
																							_snake_case: str	       =		False
																							_snake_case: Tuple	       =		main_layer_class(__snake_case							,  embed_tokens=__snake_case							)
																	else:
																							_snake_case: List[Any]	       =		main_layer_class(__snake_case							)

																	_snake_case: Tuple	       =		{
																	    name: tf.keras.Input(tensor.shape[1:]							,  dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
																	}

																	if hasattr(self.model_tester							,  'num_labels'							):
																							_snake_case: Dict	       =		self.model_tester.num_labels
																	else:
																							_snake_case: Tuple	       =		2

																	_snake_case: Tuple	       =		tf.data.Dataset.from_tensor_slices(
																	    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)							))							).batch(1							)

																	_snake_case: List[str]	       =		main_layer(__snake_case							)[0]
																	_snake_case: List[Any]	       =		tf.keras.layers.Dense(__snake_case							,  activation='softmax'							,  name='outputs'							)(__snake_case							)
																	_snake_case: List[str]	       =		tf.keras.models.Model(inputs=__snake_case							,  outputs=[outputs]							)

																	model.compile(loss='binary_crossentropy'							,  optimizer='adam'							,  metrics=['binary_accuracy']							)
																	model.fit(__snake_case							,  epochs=1							)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							_snake_case: Tuple	       =		os.path.join(__snake_case							,  'keras_model.h5'							)
																							model.save(__snake_case							)
																							if "T5" in main_layer_class.__name__:
																													_snake_case: List[str]	       =		tf.keras.models.load_model(
																													    __snake_case							,  custom_objects={
																													        main_layer_class.__name__: main_layer_class,
																													        'TFSharedEmbeddings': TFSharedEmbeddings,
																													    }							,  )
																							else:
																													_snake_case: Optional[int]	       =		tf.keras.models.load_model(
																													    __snake_case							,  custom_objects={main_layer_class.__name__: main_layer_class}							)
																							assert isinstance(__snake_case							,  tf.keras.Model							)
																							model(__snake_case							)


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[Any]	       =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes[:2]:
																	_snake_case: List[Any]	       =		model_class(__snake_case							)

																	_snake_case: Optional[int]	       =		copy.deepcopy(__snake_case							)

																	if not self.is_encoder_decoder:
																							_snake_case: Dict	       =		inputs['input_ids']
																							del inputs["input_ids"]
																	else:
																							_snake_case: Any	       =		inputs['input_ids']
																							_snake_case: List[str]	       =		inputs.get('decoder_input_ids'							,  __snake_case							)
																							del inputs["input_ids"]
																							inputs.pop('decoder_input_ids'							,  __snake_case							)

																	if not self.is_encoder_decoder:
																							_snake_case: str	       =		model.get_input_embeddings()(__snake_case							)
																	else:
																							_snake_case: Optional[int]	       =		model.get_input_embeddings()(__snake_case							)
																							_snake_case: List[str]	       =		model.get_input_embeddings()(__snake_case							)

																	_snake_case: str	       =		self._prepare_for_class(__snake_case							,  __snake_case							)

																	@tf.function
																	def run_in_graph_mode():
																							return model(__snake_case							)

																	_snake_case: Optional[Any]	       =		run_in_graph_mode()
																	self.assertIsNotNone(__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Optional[Any]							,  __snake_case						:	List[Any]							):





											'''simple docstring'''





											_snake_case: int	       =		[]
											if model.config.bos_token_id is not None:
																	special_tokens.append(model.config.bos_token_id							)
											if model.config.pad_token_id is not None:
																	special_tokens.append(model.config.pad_token_id							)
											if model.config.eos_token_id is not None:
																	special_tokens.append(model.config.eos_token_id							)

											# create random bad tokens that are not special tokens
											_snake_case: Tuple	       =		[]
											while len(__snake_case							) < num_bad_tokens:
																	_snake_case: Optional[Any]	       =		tf.squeeze(ids_tensor((1, 1)							,  self.model_tester.vocab_size							)							,  0							).numpy()[0]
																	if token not in special_tokens:
																							bad_tokens.append(__snake_case							)
											return bad_tokens


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											for token_id in output_ids[0].numpy().tolist():
																	self.assertGreaterEqual(__snake_case							,  0							)
																	self.assertLess(__snake_case							,  self.model_tester.vocab_size							)




					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	List[str]							,  __snake_case						:	int							):





											'''simple docstring'''





											for bad_word_ids in bad_words_ids:
																	# for all slices in batch
																	for generated_ids_slice in generated_ids:
																							# for all word idx
																							for i in range(len(__snake_case							)							,  len(__snake_case							)							):
																													# if tokens match
																													if generated_ids_slice[i - len(__snake_case							) : i] == bad_word_ids:
																																			return True
											return False





