
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		:   str         =    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       a_   :
			a  : Any				      =       None
			a  : Union[str, Any]				      =       ()
			a  : Tuple				      =       ()
			a  : List[str]				      =       True
			a  : Any				      =       True
			a  : int				      =       True
			a  : str				      =       False






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase=False     ):
									_lowercase									=				copy.deepcopy(__UpperCamelCase     )

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

									if return_labels:
															if model_class in get_values(__UpperCamelCase     ):
																					_lowercase									=				tf.ones(self.model_tester.batch_size							, dtype=tf.intaa     )
															elif model_class in get_values(__UpperCamelCase     ):
																					_lowercase									=				tf.zeros(self.model_tester.batch_size							, dtype=tf.intaa     )
																					_lowercase									=				tf.zeros(self.model_tester.batch_size							, dtype=tf.intaa     )
															elif model_class in [
															    *get_values(__UpperCamelCase     ),
															    *get_values(__UpperCamelCase     ),
															]:
																					_lowercase									=				tf.zeros(self.model_tester.batch_size							, dtype=tf.intaa     )
															elif model_class in get_values(__UpperCamelCase     ):
																					_lowercase									=				tf.zeros(self.model_tester.batch_size							, dtype=tf.intaa     )
															elif model_class in [
															    *get_values(__UpperCamelCase     ),
															    *get_values(__UpperCamelCase     ),
															    *get_values(__UpperCamelCase     ),
															    *get_values(__UpperCamelCase     ),
															    *get_values(__UpperCamelCase     ),
															]:
																					_lowercase									=				tf.zeros(
																					    (self.model_tester.batch_size, self.model_tester.seq_length)							, dtype=tf.intaa     )
									return inputs_dict






			@slow
			def       UpperCamelCase_							(			self     ):
									_lowercase					,	_lowercase									=				self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															_lowercase									=				self._prepare_for_class(__UpperCamelCase							, __UpperCamelCase     )
															_lowercase									=				model_class(__UpperCamelCase     )

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

															_lowercase									=				run_in_graph_mode()
															self.assertIsNotNone(__UpperCamelCase     )






			@slow
			def       UpperCamelCase_							(			self     ):
									_lowercase					,	_lowercase									=				self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															_lowercase									=				self._prepare_for_class(__UpperCamelCase							, __UpperCamelCase     )
															_lowercase									=				model_class(__UpperCamelCase     )

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

															_lowercase									=				run_in_graph_mode()
															self.assertIsNotNone(__UpperCamelCase     )






			@slow
			def       UpperCamelCase_							(			self     ):
									# This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
									_lowercase					,	_lowercase									=				self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															_lowercase									=				model_class(__UpperCamelCase     )
															if getattr(__UpperCamelCase							, """hf_compute_loss"""							, __UpperCamelCase     ):
																					# Test that model correctly compute the loss with kwargs
																					_lowercase									=				self._prepare_for_class(inputs_dict.copy()							, __UpperCamelCase							, return_labels=__UpperCamelCase     )
																					# Is there a better way to remove these decoder inputs?
																					_lowercase									=				{
																					    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""")
																					}

																					_lowercase									=				{
																					    """labels""",
																					    """label""",
																					    """label_ids""",
																					    """start_positions""",
																					    """start_position""",
																					    """end_positions""",
																					    """end_position""",
																					    """next_sentence_label""",
																					}
																					_lowercase									=				possible_label_cols.intersection(set(__UpperCamelCase     )     )
																					self.assertGreater(len(__UpperCamelCase     )							, 0							, msg="""No matching label names found!"""     )
																					_lowercase									=				{key: val for key, val in prepared_for_class.items() if key in label_names}
																					_lowercase									=				{key: val for key, val in prepared_for_class.items() if key not in label_names}
																					self.assertGreater(len(__UpperCamelCase     )							, 0     )

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

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

																					_lowercase									=				history.history["""loss"""][0]
																					self.assertTrue(not isnan(__UpperCamelCase     )     )
																					_lowercase									=				history.history["""val_loss"""][0]
																					self.assertTrue(not isnan(__UpperCamelCase     )     )






			@slow
			def       UpperCamelCase_							(			self     ):
									_lowercase					,	_lowercase									=				self.model_tester.prepare_config_and_inputs_for_common()
									_lowercase									=				True
									_lowercase									=				True

									if hasattr(__UpperCamelCase							, """use_cache"""     ):
															_lowercase									=				True

									_lowercase									=				getattr(self.model_tester							, """encoder_seq_length"""							, self.model_tester.seq_length     )
									_lowercase									=				getattr(self.model_tester							, """key_length"""							, __UpperCamelCase     )

									for model_class in self.all_model_classes[:2]:
															_lowercase									=				self._prepare_for_class(__UpperCamelCase							, __UpperCamelCase     )
															_lowercase									=				model_class(__UpperCamelCase     )
															model.build()
															_lowercase									=				len(model(__UpperCamelCase     )     )

															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:
																											_lowercase									=				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(__UpperCamelCase							, saved_model=__UpperCamelCase     )
																					_lowercase									=				os.path.join(__UpperCamelCase							, """saved_model"""							, """1"""     )
																					_lowercase									=				tf.keras.models.load_model(__UpperCamelCase     )
																					_lowercase									=				model(__UpperCamelCase     )

																					if self.is_encoder_decoder:
																											_lowercase									=				outputs["""encoder_hidden_states"""]
																											_lowercase									=				outputs["""encoder_attentions"""]
																					else:
																											_lowercase									=				outputs["""hidden_states"""]
																											_lowercase									=				outputs["""attentions"""]

																					self.assertEqual(len(__UpperCamelCase     )							, __UpperCamelCase     )

																					_lowercase									=				getattr(
																					    self.model_tester							, """expected_num_hidden_layers"""							, self.model_tester.num_hidden_layers + 1     )

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

																					self.assertEqual(len(__UpperCamelCase     )							, 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       UpperCamelCase_							(			self     ):
									tf.keras.mixed_precision.set_global_policy("""mixed_float16"""     )

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

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






			@slow
			def       UpperCamelCase_							(			self     ):
									_lowercase					,	_lowercase									=				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"]
									_lowercase									=				{
									    module_member
									    for model_class in self.all_model_classes
									    for module in (import_module(model_class.__module__     ),)
									    for module_member_name in dir(__UpperCamelCase     )
									    if module_member_name.endswith("""MainLayer"""     )
									    for module_member in (getattr(__UpperCamelCase							, __UpperCamelCase     ),)
									    if isinstance(__UpperCamelCase							, __UpperCamelCase     )
									    and tf.keras.layers.Layer in module_member.__bases__
									    and getattr(__UpperCamelCase							, """_keras_serializable"""							, __UpperCamelCase     )
									}

									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
																					_lowercase									=				TFSharedEmbeddings(self.model_tester.vocab_size							, self.model_tester.hidden_size							, name="""shared"""     )
																					_lowercase									=				False
																					_lowercase									=				main_layer_class(__UpperCamelCase							, embed_tokens=__UpperCamelCase     )
															else:
																					_lowercase									=				main_layer_class(__UpperCamelCase     )

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

															if hasattr(self.model_tester							, """num_labels"""     ):
																					_lowercase									=				self.model_tester.num_labels
															else:
																					_lowercase									=				2

															_lowercase									=				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     )

															_lowercase									=				main_layer(__UpperCamelCase     )[0]
															_lowercase									=				tf.keras.layers.Dense(__UpperCamelCase							, activation="""softmax"""							, name="""outputs"""     )(__UpperCamelCase     )
															_lowercase									=				tf.keras.models.Model(inputs=__UpperCamelCase							, outputs=[outputs]     )

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

															with tempfile.TemporaryDirectory() as tmpdirname:
																					_lowercase									=				os.path.join(__UpperCamelCase							, """keras_model.h5"""     )
																					model.save(__UpperCamelCase     )
																					if "T5" in main_layer_class.__name__:
																											_lowercase									=				tf.keras.models.load_model(
																											    __UpperCamelCase							, custom_objects={
																											        main_layer_class.__name__: main_layer_class,
																											        """TFSharedEmbeddings""": TFSharedEmbeddings,
																											    }							, )
																					else:
																											_lowercase									=				tf.keras.models.load_model(
																											    __UpperCamelCase							, custom_objects={main_layer_class.__name__: main_layer_class}     )
																					assert isinstance(__UpperCamelCase							, tf.keras.Model     )
																					model(__UpperCamelCase     )






			@slow
			def       UpperCamelCase_							(			self     ):
									_lowercase					,	_lowercase									=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes[:2]:
															_lowercase									=				model_class(__UpperCamelCase     )

															_lowercase									=				copy.deepcopy(__UpperCamelCase     )

															if not self.is_encoder_decoder:
																					_lowercase									=				inputs["""input_ids"""]
																					del inputs["input_ids"]
															else:
																					_lowercase									=				inputs["""input_ids"""]
																					_lowercase									=				inputs.get("""decoder_input_ids"""							, __UpperCamelCase     )
																					del inputs["input_ids"]
																					inputs.pop("""decoder_input_ids"""							, __UpperCamelCase     )

															if not self.is_encoder_decoder:
																					_lowercase									=				model.get_input_embeddings()(__UpperCamelCase     )
															else:
																					_lowercase									=				model.get_input_embeddings()(__UpperCamelCase     )
																					_lowercase									=				model.get_input_embeddings()(__UpperCamelCase     )

															_lowercase									=				self._prepare_for_class(__UpperCamelCase							, __UpperCamelCase     )

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

															_lowercase									=				run_in_graph_mode()
															self.assertIsNotNone(__UpperCamelCase     )






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase     ):
									# special tokens cannot be bad tokens
									_lowercase									=				[]
									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
									_lowercase									=				[]
									while len(__UpperCamelCase     ) < num_bad_tokens:
															_lowercase									=				tf.squeeze(ids_tensor((1, 1)							, self.model_tester.vocab_size     )							, 0     ).numpy()[0]
															if token not in special_tokens:
																					bad_tokens.append(__UpperCamelCase     )
									return bad_tokens






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






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase     ):
									# for all bad word tokens
									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(__UpperCamelCase     )							, len(__UpperCamelCase     )     ):
																											# if tokens match
																											if generated_ids_slice[i - len(__UpperCamelCase     ) : i] == bad_word_ids:
																																	return True
									return False

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A		:   Any         =    {'''configuration_bit''': ['''BIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BitConfig''', '''BitOnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Any         =    [
		    '''BIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BitForImageClassification''',
		    '''BitModel''',
		    '''BitPreTrainedModel''',
		    '''BitBackbone''',
		]


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Tuple         =    ['''BitImageProcessor''']


if TYPE_CHECKING:
		from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bit import (
				    BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BitBackbone,
				    BitForImageClassification,
				    BitModel,
				    BitPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_bit import BitImageProcessor

else:
		import sys

		A		:   int         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)