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


__lowercase										= ['''gpt2''']
__lowercase										= '''gpt2'''

if is_tf_available():


				class      lowerCamelCase_					(       tf.Module			):






								'''simple docstring'''






								def __init__(     self			,			__lowercase)	->     List[str]:
												super().__init__()
												__UpperCamelCase					:Any        =		tokenizer
												__UpperCamelCase					:Tuple        =		AutoConfig.from_pretrained(__lowercase)
												__UpperCamelCase					:Optional[Any]        =		TFGPTaLMHeadModel.from_config(__lowercase)







								@tf.function(input_signature=(tf.TensorSpec((None,)			,			tf.string			,			name='''text'''),))
								def       UpperCamelCase__       (     self			,			__lowercase)	->     Union[str, Any]:
												__UpperCamelCase					:Tuple        =		self.tokenizer(__lowercase)
												__UpperCamelCase					:int        =		tokenized['''input_ids'''].to_tensor()

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

												__UpperCamelCase					:int        =		self.model(input_ids=__lowercase			,			attention_mask=__lowercase)['''logits''']

												return outputs


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






				'''simple docstring'''






				def       UpperCamelCase__       (     self)	->     Tuple:
								super().setUp()

								__UpperCamelCase					:Tuple        =		[GPTaTokenizer.from_pretrained(__lowercase) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								__UpperCamelCase					:List[str]        =		[TFGPTaTokenizer.from_pretrained(__lowercase) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers) == len(self.tf_tokenizers)

								__UpperCamelCase					:List[Any]        =		[
								    '''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''',
								]
								__UpperCamelCase					:Optional[Any]        =		list(zip(self.test_sentences			,			self.test_sentences[::-1]))







				def       UpperCamelCase__       (     self)	->     Tuple:
								for tokenizer, tf_tokenizer in zip(self.tokenizers			,			self.tf_tokenizers):
												for test_inputs in self.test_sentences:
																__UpperCamelCase					:Any        =		tokenizer([test_inputs]			,			return_tensors='''tf''')
																__UpperCamelCase					:str        =		tf_tokenizer([test_inputs])

																for key in python_outputs.keys():
																				# convert them to numpy to avoid messing with ragged tensors
																				__UpperCamelCase					:Optional[Any]        =		python_outputs[key].numpy()
																				__UpperCamelCase					:Optional[int]        =		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       UpperCamelCase__       (     self)	->     str:
								for tf_tokenizer in self.tf_tokenizers:
												__UpperCamelCase					:Dict        =		tf.function(__lowercase)
												for test_inputs in self.test_sentences:
																__UpperCamelCase					:List[str]        =		tf.constant(__lowercase)
																__UpperCamelCase					:Optional[int]        =		compiled_tokenizer(__lowercase)
																__UpperCamelCase					:str        =		tf_tokenizer(__lowercase)

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







				@slow
				def       UpperCamelCase__       (     self)	->     List[Any]:
								for tf_tokenizer in self.tf_tokenizers:
												__UpperCamelCase					:Tuple        =		ModelToSave(tokenizer=__lowercase)
												__UpperCamelCase					:List[Any]        =		tf.convert_to_tensor([self.test_sentences[0]])
												__UpperCamelCase					:int        =		model.serving(__lowercase)  # Build model with some sample inputs
												with TemporaryDirectory() as tempdir:
																__UpperCamelCase					:int        =		Path(__lowercase) / '''saved.model'''
																tf.saved_model.save(__lowercase			,			__lowercase			,			signatures={'''serving_default''': model.serving})
																__UpperCamelCase					:Union[str, Any]        =		tf.saved_model.load(__lowercase)
												__UpperCamelCase					:Union[str, Any]        =		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       UpperCamelCase__       (     self)	->     str:
								for tf_tokenizer in self.tf_tokenizers:
												__UpperCamelCase					:str        =		tf.convert_to_tensor([self.test_sentences[0]])
												__UpperCamelCase					:Dict        =		tf_tokenizer(__lowercase)  # Build model with some sample inputs

												__UpperCamelCase					:Dict        =		tf_tokenizer.get_config()
												__UpperCamelCase					:Tuple        =		TFGPTaTokenizer.from_config(__lowercase)
												__UpperCamelCase					:str        =		model_from_config(__lowercase)

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







				@slow
				def       UpperCamelCase__       (     self)	->     int:
								for tf_tokenizer in self.tf_tokenizers:
												# for the test to run
												__UpperCamelCase					:Union[str, Any]        =		123_123

												for max_length in [3, 5, 1_024]:
																__UpperCamelCase					:Optional[int]        =		tf.convert_to_tensor([self.test_sentences[0]])
																__UpperCamelCase					:Tuple        =		tf_tokenizer(__lowercase			,			max_length=__lowercase)

																__UpperCamelCase					:Any        =		out['''input_ids'''].numpy().shape[1]

																assert out_length == max_length






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .notes_encoder import SpectrogramNotesEncoder
    from .continous_encoder import SpectrogramContEncoder
    from .pipeline_spectrogram_diffusion import (
        SpectrogramContEncoder,
        SpectrogramDiffusionPipeline,
        TaFilmDecoder,
    )

try:
    if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
    from .midi_utils import MidiProcessor





