


'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase				: Any							    =				logging.get_logger(__name__)

_UpperCAmelCase				: Any							    =				{
    """microsoft/unispeech-sat-base-100h-libri-ft""": (
        """https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		str	=	'unispeech-sat'




				def __init__(self       ,			__lowercase=32       ,			__lowercase=7_68       ,			__lowercase=12       ,			__lowercase=12       ,			__lowercase=30_72       ,			__lowercase="gelu"       ,			__lowercase=0.1       ,			__lowercase=0.1       ,			__lowercase=0.1       ,			__lowercase=0.0       ,			__lowercase=0.0       ,			__lowercase=0.1       ,			__lowercase=0.1       ,			__lowercase=0.0_2       ,			__lowercase=1e-5       ,			__lowercase="group"       ,			__lowercase="gelu"       ,			__lowercase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)       ,			__lowercase=(5, 2, 2, 2, 2, 2, 2)       ,			__lowercase=(10, 3, 3, 3, 3, 2, 2)       ,			__lowercase=False       ,			__lowercase=1_28       ,			__lowercase=16       ,			__lowercase=False       ,			__lowercase=True       ,			__lowercase=0.0_5       ,			__lowercase=10       ,			__lowercase=2       ,			__lowercase=0.0       ,			__lowercase=10       ,			__lowercase=0       ,			__lowercase=3_20       ,			__lowercase=2       ,			__lowercase=0.1       ,			__lowercase=1_00       ,			__lowercase=2_56       ,			__lowercase=2_56       ,			__lowercase=0.1       ,			__lowercase="mean"       ,			__lowercase=False       ,			__lowercase=False       ,			__lowercase=2_56       ,			__lowercase=(5_12, 5_12, 5_12, 5_12, 15_00)       ,			__lowercase=(5, 3, 3, 1, 1)       ,			__lowercase=(1, 2, 3, 1, 1)       ,			__lowercase=5_12       ,			__lowercase=0       ,			__lowercase=1       ,			__lowercase=2       ,			__lowercase=5_04       ,			**__lowercase       ,			):
										super().__init__(**__lowercase       ,			pad_token_id=__lowercase       ,			bos_token_id=__lowercase       ,			eos_token_id=__lowercase		)
										__lowerCAmelCase							       =				hidden_size
										__lowerCAmelCase							       =				feat_extract_norm
										__lowerCAmelCase							       =				feat_extract_activation
										__lowerCAmelCase							       =				list(__lowercase		)
										__lowerCAmelCase							       =				list(__lowercase		)
										__lowerCAmelCase							       =				list(__lowercase		)
										__lowerCAmelCase							       =				conv_bias
										__lowerCAmelCase							       =				num_conv_pos_embeddings
										__lowerCAmelCase							       =				num_conv_pos_embedding_groups
										__lowerCAmelCase							       =				len(self.conv_dim		)
										__lowerCAmelCase							       =				num_hidden_layers
										__lowerCAmelCase							       =				intermediate_size
										__lowerCAmelCase							       =				hidden_act
										__lowerCAmelCase							       =				num_attention_heads
										__lowerCAmelCase							       =				hidden_dropout
										__lowerCAmelCase							       =				attention_dropout
										__lowerCAmelCase							       =				activation_dropout
										__lowerCAmelCase							       =				feat_proj_dropout
										__lowerCAmelCase							       =				final_dropout
										__lowerCAmelCase							       =				layerdrop
										__lowerCAmelCase							       =				layer_norm_eps
										__lowerCAmelCase							       =				initializer_range
										__lowerCAmelCase							       =				vocab_size
										__lowerCAmelCase							       =				num_clusters
										__lowerCAmelCase							       =				do_stable_layer_norm
										__lowerCAmelCase							       =				use_weighted_layer_sum

										if (
										    (len(self.conv_stride		) != self.num_feat_extract_layers)
										    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
										    or (len(self.conv_dim		) != self.num_feat_extract_layers)
										):
																raise ValueError(
																    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
																    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
																    F""" {len(self.conv_dim		)}`, `len(config.conv_stride) = {len(self.conv_stride		)}`,"""
																    F""" `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										__lowerCAmelCase							       =				apply_spec_augment
										__lowerCAmelCase							       =				mask_time_prob
										__lowerCAmelCase							       =				mask_time_length
										__lowerCAmelCase							       =				mask_time_min_masks
										__lowerCAmelCase							       =				mask_feature_prob
										__lowerCAmelCase							       =				mask_feature_length
										__lowerCAmelCase							       =				mask_feature_min_masks

										# parameters for pretraining with codevector quantized representations
										__lowerCAmelCase							       =				num_codevectors_per_group
										__lowerCAmelCase							       =				num_codevector_groups
										__lowerCAmelCase							       =				contrastive_logits_temperature
										__lowerCAmelCase							       =				feat_quantizer_dropout
										__lowerCAmelCase							       =				num_negatives
										__lowerCAmelCase							       =				codevector_dim
										__lowerCAmelCase							       =				proj_codevector_dim
										__lowerCAmelCase							       =				diversity_loss_weight

										# ctc loss
										__lowerCAmelCase							       =				ctc_loss_reduction
										__lowerCAmelCase							       =				ctc_zero_infinity

										# SequenceClassification-specific parameter. Feel free to ignore for other classes.
										__lowerCAmelCase							       =				classifier_proj_size

										# XVector-specific parameters. Feel free to ignore for other classes.
										__lowerCAmelCase							       =				list(__lowercase		)
										__lowerCAmelCase							       =				list(__lowercase		)
										__lowerCAmelCase							       =				list(__lowercase		)
										__lowerCAmelCase							       =				xvector_output_dim




				@property
				def 					_snake_case  (self		):
										return functools.reduce(operator.mul       ,			self.conv_stride       ,			1		)





'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_keras_nlp_available():
	from transformers.models.gpta import TFGPTaTokenizer


_UpperCAmelCase				: str							    =				["""gpt2"""]
_UpperCAmelCase				: Optional[int]							    =				"""gpt2"""

if is_tf_available():


	class       a__				( tf.Module      ):







					"""simple docstring"""



					def __init__(self       ,			__lowercase		):
											super().__init__()
											__lowerCAmelCase							       =				tokenizer
											__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
											__lowerCAmelCase							       =				TFGPTaLMHeadModel.from_config(__lowercase		)




					@tf.function(input_signature=(tf.TensorSpec((None,)       ,			tf.string       ,			name='''text'''		),)		)
					def 					_snake_case  (self       ,			__lowercase		):
											__lowerCAmelCase							       =				self.tokenizer(__lowercase		)
											__lowerCAmelCase							       =				tokenized['''input_ids'''].to_tensor()

											__lowerCAmelCase							       =				tf.cast(input_ids_dense > 0       ,			tf.intaa		)
											# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

											__lowerCAmelCase							       =				self.model(input_ids=__lowercase       ,			attention_mask=__lowercase		)['''logits''']

											return outputs


@require_tf
@require_keras_nlp
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										super().setUp()

										__lowerCAmelCase							       =				[GPTaTokenizer.from_pretrained(__lowercase		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
										__lowerCAmelCase							       =				[TFGPTaTokenizer.from_pretrained(__lowercase		) for checkpoint in TOKENIZER_CHECKPOINTS]
										assert len(self.tokenizers		) == len(self.tf_tokenizers		)

										__lowerCAmelCase							       =				[
										    '''This is a straightforward English test sentence.''',
										    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
										    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
										    '''And some much more rare Chinese: 齉 堃 齉堃''',
										    '''Je vais aussi écrire en français pour tester les accents''',
										    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
										]
										__lowerCAmelCase							       =				list(zip(self.test_sentences       ,			self.test_sentences[::-1]		)		)




				def 					_snake_case  (self		):
										for tokenizer, tf_tokenizer in zip(self.tokenizers       ,			self.tf_tokenizers		):
																for test_inputs in self.test_sentences:
																						__lowerCAmelCase							       =				tokenizer([test_inputs]       ,			return_tensors='''tf'''		)
																						__lowerCAmelCase							       =				tf_tokenizer([test_inputs]		)

																						for key in python_outputs.keys():
																												# convert them to numpy to avoid messing with ragged tensors
																												__lowerCAmelCase							       =				python_outputs[key].numpy()
																												__lowerCAmelCase							       =				tf_outputs[key].numpy()

																												self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
																												self.assertTrue(tf.reduce_all(tf.cast(__lowercase       ,			tf.intaa		) == tf_outputs_values		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				tf.function(__lowercase		)
																for test_inputs in self.test_sentences:
																						__lowerCAmelCase							       =				tf.constant(__lowercase		)
																						__lowerCAmelCase							       =				compiled_tokenizer(__lowercase		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase		)

																						for key in eager_outputs.keys():
																												self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				ModelToSave(tokenizer=__lowercase		)
																__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																__lowerCAmelCase							       =				model.serving(__lowercase		)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																						__lowerCAmelCase							       =				Path(__lowercase		) / '''saved.model'''
																						tf.saved_model.save(__lowercase       ,			__lowercase       ,			signatures={'''serving_default''': model.serving}		)
																						__lowerCAmelCase							       =				tf.saved_model.load(__lowercase		)
																__lowerCAmelCase							       =				loaded_model.signatures['''serving_default'''](__lowercase		)['''output_0''']
																# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																self.assertTrue(tf.reduce_all(out == loaded_output		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																__lowerCAmelCase							       =				tf_tokenizer(__lowercase		)  # Build model with some sample inputs

																__lowerCAmelCase							       =				tf_tokenizer.get_config()
																__lowerCAmelCase							       =				TFGPTaTokenizer.from_config(__lowercase		)
																__lowerCAmelCase							       =				model_from_config(__lowercase		)

																for key in from_config_output.keys():
																						self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																# for the test to run
																__lowerCAmelCase							       =				12_31_23

																for max_length in [3, 5, 10_24]:
																						__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase       ,			max_length=__lowercase		)

																						__lowerCAmelCase							       =				out['''input_ids'''].numpy().shape[1]

																						assert out_length == max_length

