

"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


__A	:       str        =						random.Random()
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__=1.0     ,				lowercase__=None     ,				lowercase__=None ):
					"""simple docstring"""
					if rng is None:
										A     =   global_rng

					A     =   []
					for batch_idx in range(shape[0] ):
										values.append([] )
										for _ in range(shape[1] ):
															values[-1].append(rng.random() * scale )

					return values





@require_torch
class      __UpperCamelCase					(  unittest.TestCase		):


		def __init__(self					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=7	,	__SCREAMING_SNAKE_CASE					:						List[Any]=4_0_0	,	__SCREAMING_SNAKE_CASE					:						str=2_0_0_0	,	__SCREAMING_SNAKE_CASE					:						List[str]=1	,	__SCREAMING_SNAKE_CASE					:						Tuple=0.0	,	__SCREAMING_SNAKE_CASE					:						Any=2_4_0_0_0	,	__SCREAMING_SNAKE_CASE					:						Optional[int]=True	,	):
							A     =   parent
							A     =   batch_size
							A     =   min_seq_length
							A     =   max_seq_length
							A     =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							A     =   feature_size
							A     =   padding_value
							A     =   sampling_rate
							A     =   return_attention_mask


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	__SCREAMING_SNAKE_CASE					:						List[Any]=False	,	__SCREAMING_SNAKE_CASE					:						Any=False):
							def _flatten(__SCREAMING_SNAKE_CASE					:						List[Any]):
												return list(itertools.chain(*__SCREAMING_SNAKE_CASE))

							if equal_length:
												A     =   floats_list((self.batch_size, self.max_seq_length))
							else:
												# make sure that inputs increase in size
												A     =   [
												    _flatten(floats_list((x, self.feature_size)))
												    for x in range(self.min_seq_length	,	self.max_seq_length	,	self.seq_length_diff)
												]

							if numpify:
												A     =   [np.asarray(__SCREAMING_SNAKE_CASE) for x in audio_inputs]

							return audio_inputs





@require_torch
class      __UpperCamelCase					(  _A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       EncodecFeatureExtractor


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   EnCodecFeatureExtractionTester(self)


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							# Tests that all call wrap to encode_plus and batch_encode_plus
							A     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
							# create three inputs of length 800, 1000, and 1200
							A     =   [floats_list((1, x))[0] for x in range(8_0_0	,	1_4_0_0	,	2_0_0)]
							A     =   [np.asarray(__SCREAMING_SNAKE_CASE) for audio_input in audio_inputs]

							# Test not batched input
							A     =   feat_extract(audio_inputs[0]	,	return_tensors="np").input_values
							A     =   feat_extract(np_audio_inputs[0]	,	return_tensors="np").input_values
							self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	atol=1E-3))

							# Test batched
							A     =   feat_extract(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="np").input_values
							A     =   feat_extract(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="np").input_values
							for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE):
												self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	atol=1E-3))


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
							A     =   np.random.rand(1_0_0).astype(np.floataa)
							A     =   np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_inputs]:
												A     =   feature_extractor.pad([{"input_values": inputs}]	,	return_tensors="np")
												self.assertTrue(np_processed.input_values.dtype == np.floataa)
												A     =   feature_extractor.pad([{"input_values": inputs}]	,	return_tensors="pt")
												self.assertTrue(pt_processed.input_values.dtype == torch.floataa)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Optional[int]):
							from datasets import load_dataset

							A     =   load_dataset("hf-internal-testing/librispeech_asr_dummy"	,	"clean"	,	split="validation")
							# automatic decoding with librispeech
							A     =   ds.sort("id").select(range(__SCREAMING_SNAKE_CASE))[:num_samples]["audio"]

							return [x["array"] for x in audio_samples]


		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							# fmt: off
							A     =   torch.tensor(
							    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
							     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
							     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
							     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
							     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
							     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
							# fmt: on
							A     =   self._load_datasamples(1)
							A     =   EncodecFeatureExtractor()
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(input_values.shape	,	(1, 1, 9_3_6_8_0))
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0]	,	__SCREAMING_SNAKE_CASE	,	atol=1E-6))


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							# fmt: off
							A     =   torch.tensor(
							    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
							     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
							     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
							     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
							     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
							     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
							# fmt: on
							A     =   self._load_datasamples(1)
							A     =   [np.tile(input_audio[0][None]	,	reps=(2, 1))]
							input_audio[0][1] *= 0.5
							A     =   EncodecFeatureExtractor(feature_size=2)
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(input_values.shape	,	(1, 2, 9_3_6_8_0))
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0]	,	__SCREAMING_SNAKE_CASE	,	atol=1E-6))
							self.assertTrue(torch.allclose(input_values[0, 1, :3_0]	,	EXPECTED_INPUT_VALUES * 0.5	,	atol=1E-6))


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self._load_datasamples(2)
							# would be easier if the stride was like
							A     =   EncodecFeatureExtractor(feature_size=1	,	chunk_length_s=1	,	overlap=0.0_1)

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Both padding and truncation were set. Make sure you only set one.$"	,	):
												A     =   feature_extractor(
												    __SCREAMING_SNAKE_CASE	,	padding="max_length"	,	truncation=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							# truncate to chunk
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	truncation=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 7_1_5_2_0))  # 2 chunks

							# force truncate to max_length
							A     =   feature_extractor(
							    __SCREAMING_SNAKE_CASE	,	truncation=__SCREAMING_SNAKE_CASE	,	max_length=4_8_0_0_0	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 4_8_0_0_0))

							# pad to chunk
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(padded_outputs.shape	,	(2, 1, 9_5_2_8_0))

							# pad to chunk
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 9_5_2_8_0))

							# force pad to max length
							A     =   feature_extractor(
							    __SCREAMING_SNAKE_CASE	,	padding="max_length"	,	max_length=1_0_0_0_0_0	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 1_0_0_0_0_0))

							# force no pad
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,	):
												A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							A     =   feature_extractor(input_audio[0]	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(1, 1, 9_3_6_8_0))

							# no pad if no chunk_length_s
							A     =   None
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,	):
												A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							A     =   feature_extractor(input_audio[0]	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(1, 1, 9_3_6_8_0))

							# no pad if no overlap
							A     =   2
							A     =   None
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,	):
												A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							A     =   feature_extractor(input_audio[0]	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(1, 1, 9_3_6_8_0))










"""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:					Optional[int]     				=	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:					List[str]     				=	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__		:
   _lowerCamelCase       				=					None
   _lowerCamelCase       				=					()
   _lowerCamelCase       				=					()
   _lowerCamelCase       				=					True
   _lowerCamelCase       				=					True
   _lowerCamelCase       				=					True
   _lowerCamelCase       				=					False


   def 							lowercase					(				self   :				Dict,  lowerCAmelCase   :				Dict,  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				Optional[int]=False      )     ->							dict:
         lowercase	:   Tuple											= copy.deepcopy(lowerCAmelCase      )

         if model_class in get_values(lowerCAmelCase      ):
               lowercase	:   Union[str, Any]											= {
                   k: tf.tile(tf.expand_dims(lowerCAmelCase,  1      ),  (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)      )
                   if isinstance(lowerCAmelCase,  tf.Tensor      ) and v.ndim > 0
                   else v
                   for k, v in inputs_dict.items()
               }

         if return_labels:
               if model_class in get_values(lowerCAmelCase      ):
                     lowercase	:   List[str]											= tf.ones(self.model_tester.batch_size,  dtype=tf.intaa      )
               elif model_class in get_values(lowerCAmelCase      ):
                     lowercase	:   Optional[Any]											= tf.zeros(self.model_tester.batch_size,  dtype=tf.intaa      )
                     lowercase	:   Union[str, Any]											= tf.zeros(self.model_tester.batch_size,  dtype=tf.intaa      )
               elif model_class in [
                   *get_values(lowerCAmelCase      ),
                   *get_values(lowerCAmelCase      ),
               ]:
                     lowercase	:   str											= tf.zeros(self.model_tester.batch_size,  dtype=tf.intaa      )
               elif model_class in get_values(lowerCAmelCase      ):
                     lowercase	:   str											= tf.zeros(self.model_tester.batch_size,  dtype=tf.intaa      )
               elif model_class in [
                   *get_values(lowerCAmelCase      ),
                   *get_values(lowerCAmelCase      ),
                   *get_values(lowerCAmelCase      ),
                   *get_values(lowerCAmelCase      ),
                   *get_values(lowerCAmelCase      ),
               ]:
                     lowercase	:   Tuple											= tf.zeros(
                         (self.model_tester.batch_size, self.model_tester.seq_length),  dtype=tf.intaa      )
         return inputs_dict


   @slow
   def 							lowercase					(				self   :				int      )     ->							Union[str, Any]:
         lowercase						,     lowercase	:   Optional[int]											= self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes[:2]:
               lowercase	:   List[str]											= self._prepare_for_class(lowerCAmelCase,  lowerCAmelCase      )
               lowercase	:   List[Any]											= model_class(lowerCAmelCase      )

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

               lowercase	:   Optional[Any]											= run_in_graph_mode()
               self.assertIsNotNone(lowerCAmelCase      )


   @slow
   def 							lowercase					(				self   :				List[str]      )     ->							Optional[int]:
         lowercase						,     lowercase	:   Tuple											= self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes[:2]:
               lowercase	:   Optional[Any]											= self._prepare_for_class(lowerCAmelCase,  lowerCAmelCase      )
               lowercase	:   int											= model_class(lowerCAmelCase      )

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

               lowercase	:   int											= run_in_graph_mode()
               self.assertIsNotNone(lowerCAmelCase      )


   @slow
   def 							lowercase					(				self   :				List[Any]      )     ->							Any:
         # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
         lowercase						,     lowercase	:   Tuple											= self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes[:2]:
               lowercase	:   Optional[Any]											= model_class(lowerCAmelCase      )
               if getattr(lowerCAmelCase,  'hf_compute_loss',  lowerCAmelCase      ):
                     # Test that model correctly compute the loss with kwargs
                     lowercase	:   int											= self._prepare_for_class(inputs_dict.copy(),  lowerCAmelCase,  return_labels=lowerCAmelCase      )
                     # Is there a better way to remove these decoder inputs?
                     lowercase	:   Dict											= {
                         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	:   int											= {
                         'labels',
                         'label',
                         'label_ids',
                         'start_positions',
                         'start_position',
                         'end_positions',
                         'end_position',
                         'next_sentence_label',
                     }
                     lowercase	:   int											= possible_label_cols.intersection(set(lowerCAmelCase      )      )
                     self.assertGreater(len(lowerCAmelCase      ),  0,  msg='No matching label names found!'      )
                     lowercase	:   Union[str, Any]											= {key: val for key, val in prepared_for_class.items() if key in label_names}
                     lowercase	:   Tuple											= {key: val for key, val in prepared_for_class.items() if key not in label_names}
                     self.assertGreater(len(lowerCAmelCase      ),  0      )

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

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

                     lowercase	:   str											= history.history['loss'][0]
                     self.assertTrue(not isnan(lowerCAmelCase      )      )
                     lowercase	:   Optional[int]											= history.history['val_loss'][0]
                     self.assertTrue(not isnan(lowerCAmelCase      )      )


   @slow
   def 							lowercase					(				self   :				Optional[Any]      )     ->							Tuple:
         lowercase						,     lowercase	:   int											= self.model_tester.prepare_config_and_inputs_for_common()
         lowercase	:   List[str]											= True
         lowercase	:   Dict											= True

         if hasattr(lowerCAmelCase,  'use_cache'      ):
               lowercase	:   Optional[int]											= True

         lowercase	:   Tuple											= getattr(self.model_tester,  'encoder_seq_length',  self.model_tester.seq_length      )
         lowercase	:   Tuple											= getattr(self.model_tester,  'key_length',  lowerCAmelCase      )

         for model_class in self.all_model_classes[:2]:
               lowercase	:   Any											= self._prepare_for_class(lowerCAmelCase,  lowerCAmelCase      )
               lowercase	:   Optional[int]											= model_class(lowerCAmelCase      )
               model.build()
               lowercase	:   Dict											= len(model(lowerCAmelCase      )      )

               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	:   Tuple											= 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(lowerCAmelCase,  saved_model=lowerCAmelCase      )
                     lowercase	:   Any											= os.path.join(lowerCAmelCase,  'saved_model',  '1'      )
                     lowercase	:   List[Any]											= tf.keras.models.load_model(lowerCAmelCase      )
                     lowercase	:   Dict											= model(lowerCAmelCase      )

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

                     self.assertEqual(len(lowerCAmelCase      ),  lowerCAmelCase      )

                     lowercase	:   Optional[Any]											= getattr(
                         self.model_tester,  'expected_num_hidden_layers',  self.model_tester.num_hidden_layers + 1      )

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

                     self.assertEqual(len(lowerCAmelCase      ),  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 							lowercase					(				self   :				Dict      )     ->							Union[str, Any]:
         tf.keras.mixed_precision.set_global_policy('mixed_float16'      )

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

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


   @slow
   def 							lowercase					(				self   :				Dict      )     ->							List[str]:
         lowercase						,     lowercase	:   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"]
         lowercase	:   Dict											= {
             module_member
             for model_class in self.all_model_classes
             for module in (import_module(model_class.__module__      ),)
             for module_member_name in dir(lowerCAmelCase      )
             if module_member_name.endswith('MainLayer'      )
             for module_member in (getattr(lowerCAmelCase,  lowerCAmelCase      ),)
             if isinstance(lowerCAmelCase,  lowerCAmelCase      )
             and tf.keras.layers.Layer in module_member.__bases__
             and getattr(lowerCAmelCase,  '_keras_serializable',  lowerCAmelCase      )
         }

         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	:   List[Any]											= TFSharedEmbeddings(self.model_tester.vocab_size,  self.model_tester.hidden_size,  name='shared'      )
                     lowercase	:   str											= False
                     lowercase	:   Any											= main_layer_class(lowerCAmelCase,  embed_tokens=lowerCAmelCase      )
               else:
                     lowercase	:   Tuple											= main_layer_class(lowerCAmelCase      )

               lowercase	:   Optional[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'      ):
                     lowercase	:   int											= self.model_tester.num_labels
               else:
                     lowercase	:   Optional[int]											= 2

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

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

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


   @slow
   def 							lowercase					(				self   :				Tuple      )     ->							List[str]:
         lowercase						,     lowercase	:   Any											= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes[:2]:
               lowercase	:   Any											= model_class(lowerCAmelCase      )

               lowercase	:   List[str]											= copy.deepcopy(lowerCAmelCase      )

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

               if not self.is_encoder_decoder:
                     lowercase	:   Union[str, Any]											= model.get_input_embeddings()(lowerCAmelCase      )
               else:
                     lowercase	:   Dict											= model.get_input_embeddings()(lowerCAmelCase      )
                     lowercase	:   Any											= model.get_input_embeddings()(lowerCAmelCase      )

               lowercase	:   Union[str, Any]											= self._prepare_for_class(lowerCAmelCase,  lowerCAmelCase      )

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

               lowercase	:   int											= run_in_graph_mode()
               self.assertIsNotNone(lowerCAmelCase      )


   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				int      )     ->							Dict:
         # special tokens cannot be bad tokens
         lowercase	:   List[Any]											= []
         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	:   List[Any]											= []
         while len(lowerCAmelCase      ) < num_bad_tokens:
               lowercase	:   str											= tf.squeeze(ids_tensor((1, 1),  self.model_tester.vocab_size      ),  0      ).numpy()[0]
               if token not in special_tokens:
                     bad_tokens.append(lowerCAmelCase      )
         return bad_tokens


   def 							lowercase					(				self   :				List[str],  lowerCAmelCase   :				Any      )     ->							str:
         for token_id in output_ids[0].numpy().tolist():
               self.assertGreaterEqual(lowerCAmelCase,  0      )
               self.assertLess(lowerCAmelCase,  self.model_tester.vocab_size      )






   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				Any,  lowerCAmelCase   :				str      )     ->							Any:
         # 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(lowerCAmelCase      ),  len(lowerCAmelCase      )      ):
                           # if tokens match
                           if generated_ids_slice[i - len(lowerCAmelCase      ) : i] == bad_word_ids:
                                 return True
         return False
