"""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:
								UpperCamelCase     					=     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)]
																)
																UpperCamelCase     					=     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   UpperCAmelCase__				:


	"""simple docstring"""
	lowerCAmelCase__    :					int						      =							None
	lowerCAmelCase__    :					int						      =							()
	lowerCAmelCase__    :					int						      =							()
	lowerCAmelCase__    :					Tuple						      =							True
	lowerCAmelCase__    :					Optional[Any]						      =							True
	lowerCAmelCase__    :					Any						      =							True
	lowerCAmelCase__    :					Optional[int]						      =							False







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE=False )    ->	List[Any]:
							a_							:			List[str]     		=					copy.deepcopy(_SCREAMING_SNAKE_CASE )

							if model_class in get_values(_SCREAMING_SNAKE_CASE ):
													a_							:			Union[str, Any]     		=					{
													    k: tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE       ,     1 )       ,     (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
													    if isinstance(_SCREAMING_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(_SCREAMING_SNAKE_CASE ):
																			a_							:			str     		=					tf.ones(self.model_tester.batch_size       ,     dtype=tf.intaa )
													elif model_class in get_values(_SCREAMING_SNAKE_CASE ):
																			a_							:			str     		=					tf.zeros(self.model_tester.batch_size       ,     dtype=tf.intaa )
																			a_							:			Union[str, Any]     		=					tf.zeros(self.model_tester.batch_size       ,     dtype=tf.intaa )
													elif model_class in [
													    *get_values(_SCREAMING_SNAKE_CASE ),
													    *get_values(_SCREAMING_SNAKE_CASE ),
													]:
																			a_							:			List[str]     		=					tf.zeros(self.model_tester.batch_size       ,     dtype=tf.intaa )
													elif model_class in get_values(_SCREAMING_SNAKE_CASE ):
																			a_							:			List[Any]     		=					tf.zeros(self.model_tester.batch_size       ,     dtype=tf.intaa )
													elif model_class in [
													    *get_values(_SCREAMING_SNAKE_CASE ),
													    *get_values(_SCREAMING_SNAKE_CASE ),
													    *get_values(_SCREAMING_SNAKE_CASE ),
													    *get_values(_SCREAMING_SNAKE_CASE ),
													    *get_values(_SCREAMING_SNAKE_CASE ),
													]:
																			a_							:			Optional[int]     		=					tf.zeros(
																			    (self.model_tester.batch_size, self.model_tester.seq_length)       ,     dtype=tf.intaa )
							return inputs_dict







	@slow
	def       A    (			self )    ->	Optional[Any]:
							a_							:			Optional[Any]     		=					self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
													a_							:			str     		=					self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
													a_							:			Optional[Any]     		=					model_class(_SCREAMING_SNAKE_CASE )

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

													a_							:			int     		=					run_in_graph_mode()
													self.assertIsNotNone(_SCREAMING_SNAKE_CASE )







	@slow
	def       A    (			self )    ->	Optional[int]:
							a_							:			Dict     		=					self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
													a_							:			List[Any]     		=					self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
													a_							:			int     		=					model_class(_SCREAMING_SNAKE_CASE )

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

													a_							:			List[Any]     		=					run_in_graph_mode()
													self.assertIsNotNone(_SCREAMING_SNAKE_CASE )







	@slow
	def       A    (			self )    ->	str:
							a_							:			Optional[int]     		=					self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
													a_							:			Dict     		=					model_class(_SCREAMING_SNAKE_CASE )
													if getattr(_SCREAMING_SNAKE_CASE       ,     "hf_compute_loss"       ,     _SCREAMING_SNAKE_CASE ):
																			# Test that model correctly compute the loss with kwargs
																			a_							:			Dict     		=					self._prepare_for_class(inputs_dict.copy()       ,     _SCREAMING_SNAKE_CASE       ,     return_labels=_SCREAMING_SNAKE_CASE )
																			# Is there a better way to remove these decoder inputs?
																			a_							:			Optional[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''')
																			}

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

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

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

																			a_							:			int     		=					history.history['''loss'''][0]
																			self.assertTrue(not isnan(_SCREAMING_SNAKE_CASE ) )
																			a_							:			Any     		=					history.history['''val_loss'''][0]
																			self.assertTrue(not isnan(_SCREAMING_SNAKE_CASE ) )







	@slow
	def       A    (			self )    ->	Optional[int]:
							a_							:			Dict     		=					self.model_tester.prepare_config_and_inputs_for_common()
							a_							:			Any     		=					True
							a_							:			Dict     		=					True

							if hasattr(_SCREAMING_SNAKE_CASE       ,     "use_cache" ):
													a_							:			Optional[Any]     		=					True

							a_							:			Union[str, Any]     		=					getattr(self.model_tester       ,     "encoder_seq_length"       ,     self.model_tester.seq_length )
							a_							:			List[Any]     		=					getattr(self.model_tester       ,     "key_length"       ,     _SCREAMING_SNAKE_CASE )

							for model_class in self.all_model_classes[:2]:
													a_							:			Any     		=					self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
													a_							:			Union[str, Any]     		=					model_class(_SCREAMING_SNAKE_CASE )
													model.build()
													a_							:			Union[str, Any]     		=					len(model(_SCREAMING_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:
																									a_							:			Union[str, Any]     		=					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(_SCREAMING_SNAKE_CASE       ,     saved_model=_SCREAMING_SNAKE_CASE )
																			a_							:			List[str]     		=					os.path.join(_SCREAMING_SNAKE_CASE       ,     "saved_model"       ,     "1" )
																			a_							:			int     		=					tf.keras.models.load_model(_SCREAMING_SNAKE_CASE )
																			a_							:			Optional[Any]     		=					model(_SCREAMING_SNAKE_CASE )

																			if self.is_encoder_decoder:
																									a_							:			str     		=					outputs['''encoder_hidden_states''']
																									a_							:			str     		=					outputs['''encoder_attentions''']
																			else:
																									a_							:			Optional[int]     		=					outputs['''hidden_states''']
																									a_							:			Tuple     		=					outputs['''attentions''']

																			self.assertEqual(len(_SCREAMING_SNAKE_CASE )       ,     _SCREAMING_SNAKE_CASE )

																			a_							:			Dict     		=					getattr(
																			    self.model_tester       ,     "expected_num_hidden_layers"       ,     self.model_tester.num_hidden_layers + 1 )

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

																			self.assertEqual(len(_SCREAMING_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       A    (			self )    ->	Optional[int]:
							tf.keras.mixed_precision.set_global_policy("mixed_float16" )

							# try/finally block to ensure subsequent tests run in float32
							try:
													a_							:			str     		=					self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																			a_							:			int     		=					self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
																			a_							:			Tuple     		=					model_class(_SCREAMING_SNAKE_CASE )
																			a_							:			int     		=					model(_SCREAMING_SNAKE_CASE )

																			self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
							finally:
													tf.keras.mixed_precision.set_global_policy("float32" )







	@slow
	def       A    (			self )    ->	Optional[int]:
							a_							:			Dict     		=					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"]
							a_							:			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(_SCREAMING_SNAKE_CASE )
							    if module_member_name.endswith("MainLayer" )
							    for module_member in (getattr(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ),)
							    if isinstance(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
							    and tf.keras.layers.Layer in module_member.__bases__
							    and getattr(_SCREAMING_SNAKE_CASE       ,     "_keras_serializable"       ,     _SCREAMING_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
																			a_							:			Tuple     		=					TFSharedEmbeddings(self.model_tester.vocab_size       ,     self.model_tester.hidden_size       ,     name="shared" )
																			a_							:			List[Any]     		=					False
																			a_							:			Any     		=					main_layer_class(_SCREAMING_SNAKE_CASE       ,     embed_tokens=_SCREAMING_SNAKE_CASE )
													else:
																			a_							:			Optional[Any]     		=					main_layer_class(_SCREAMING_SNAKE_CASE )

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

													if hasattr(self.model_tester       ,     "num_labels" ):
																			a_							:			Optional[Any]     		=					self.model_tester.num_labels
													else:
																			a_							:			Tuple     		=					2

													a_							:			Optional[int]     		=					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 )

													a_							:			Union[str, Any]     		=					main_layer(_SCREAMING_SNAKE_CASE )[0]
													a_							:			str     		=					tf.keras.layers.Dense(_SCREAMING_SNAKE_CASE       ,     activation="softmax"       ,     name="outputs" )(_SCREAMING_SNAKE_CASE )
													a_							:			Dict     		=					tf.keras.models.Model(inputs=_SCREAMING_SNAKE_CASE       ,     outputs=[outputs] )

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

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







	@slow
	def       A    (			self )    ->	Union[str, Any]:
							a_							:			Optional[Any]     		=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:2]:
													a_							:			int     		=					model_class(_SCREAMING_SNAKE_CASE )

													a_							:			Tuple     		=					copy.deepcopy(_SCREAMING_SNAKE_CASE )

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

													if not self.is_encoder_decoder:
																			a_							:			str     		=					model.get_input_embeddings()(_SCREAMING_SNAKE_CASE )
													else:
																			a_							:			Dict     		=					model.get_input_embeddings()(_SCREAMING_SNAKE_CASE )
																			a_							:			str     		=					model.get_input_embeddings()(_SCREAMING_SNAKE_CASE )

													a_							:			List[str]     		=					self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )

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

													a_							:			int     		=					run_in_graph_mode()
													self.assertIsNotNone(_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	List[str]:
							a_							:			Dict     		=					[]
							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
							a_							:			Optional[Any]     		=					[]
							while len(_SCREAMING_SNAKE_CASE ) < num_bad_tokens:
													a_							:			Dict     		=					tf.squeeze(ids_tensor((1, 1)       ,     self.model_tester.vocab_size )       ,     0 ).numpy()[0]
													if token not in special_tokens:
																			bad_tokens.append(_SCREAMING_SNAKE_CASE )
							return bad_tokens







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	int:



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







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Dict:



							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(_SCREAMING_SNAKE_CASE )       ,     len(_SCREAMING_SNAKE_CASE ) ):
																									# if tokens match
																									if generated_ids_slice[i - len(_SCREAMING_SNAKE_CASE ) : i] == bad_word_ids:
																															return True
							return False





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase     					=     {
    'configuration_bark': [
        'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BarkCoarseConfig',
        'BarkConfig',
        'BarkFineConfig',
        'BarkSemanticConfig',
    ],
    'processing_bark': ['BarkProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BarkFineModel',
				    'BarkSemanticModel',
				    'BarkCoarseModel',
				    'BarkModel',
				    'BarkPreTrainedModel',
				    'BarkCausalModel',
				]

if TYPE_CHECKING:
				from .configuration_bark import (
				    BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BarkCoarseConfig,
				    BarkConfig,
				    BarkFineConfig,
				    BarkSemanticConfig,
				)
				from .processing_bark import BarkProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bark import (
								    BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BarkCausalModel,
								    BarkCoarseModel,
								    BarkFineModel,
								    BarkModel,
								    BarkPreTrainedModel,
								    BarkSemanticModel,
								)

else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




