



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


_snake_case		=  ["gpt2"]
_snake_case		=  "gpt2"

if is_tf_available():



						class   UpperCAmelCase_    (       tf.Module):







													def __init__(   self,			__a):

																				'''simple docstring'''

																				super().__init__()
																				_lowerCAmelCase     :							Optional[Any]    =    tokenizer
																				_lowerCAmelCase     :							int    =    AutoConfig.from_pretrained(lowerCAmelCase__)
																				_lowerCAmelCase     :							Optional[int]    =    TFGPTaLMHeadModel.from_config(lowerCAmelCase__)







													@tf.function(input_signature=(tf.TensorSpec((None,),			tf.string,			name="text"),))
													def    snake_case__      (   self,			__a):

																				'''simple docstring'''

																				_lowerCAmelCase     :							Dict    =    self.tokenizer(lowerCAmelCase__)
																				_lowerCAmelCase     :							Dict    =    tokenized["input_ids"].to_tensor()

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

																				_lowerCAmelCase     :							Tuple    =    self.model(input_ids=lowerCAmelCase__,			attention_mask=lowerCAmelCase__)["logits"]

																				return outputs



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







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

														_lowerCAmelCase     :							Dict    =    [GPTaTokenizer.from_pretrained(lowerCAmelCase__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
														_lowerCAmelCase     :							Any    =    [TFGPTaTokenizer.from_pretrained(lowerCAmelCase__) for checkpoint in TOKENIZER_CHECKPOINTS]
														assert len(self.tokenizers) == len(self.tf_tokenizers)

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







							def    snake_case__      (   self):

														'''simple docstring'''

														for tokenizer, tf_tokenizer in zip(self.tokenizers,			self.tf_tokenizers):
																					for test_inputs in self.test_sentences:
																												_lowerCAmelCase     :							List[str]    =    tokenizer([test_inputs],			return_tensors="tf")
																												_lowerCAmelCase     :							Tuple    =    tf_tokenizer([test_inputs])

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

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







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase     :							Union[str, Any]    =    tf.function(lowerCAmelCase__)
																					for test_inputs in self.test_sentences:
																												_lowerCAmelCase     :							List[Any]    =    tf.constant(lowerCAmelCase__)
																												_lowerCAmelCase     :							Optional[Any]    =    compiled_tokenizer(lowerCAmelCase__)
																												_lowerCAmelCase     :							Union[str, Any]    =    tf_tokenizer(lowerCAmelCase__)

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







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase     :							Tuple    =    ModelToSave(tokenizer=lowerCAmelCase__)
																					_lowerCAmelCase     :							Optional[Any]    =    tf.convert_to_tensor([self.test_sentences[0]])
																					_lowerCAmelCase     :							Any    =    model.serving(lowerCAmelCase__)  # Build model with some sample inputs
																					with TemporaryDirectory() as tempdir:
																												_lowerCAmelCase     :							Optional[int]    =    Path(lowerCAmelCase__) / "saved.model"
																												tf.saved_model.save(lowerCAmelCase__,			lowerCAmelCase__,			signatures={"serving_default": model.serving})
																												_lowerCAmelCase     :							Any    =    tf.saved_model.load(lowerCAmelCase__)
																					_lowerCAmelCase     :							Tuple    =    loaded_model.signatures["serving_default"](lowerCAmelCase__)["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):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase     :							Optional[int]    =    tf.convert_to_tensor([self.test_sentences[0]])
																					_lowerCAmelCase     :							Optional[Any]    =    tf_tokenizer(lowerCAmelCase__)  # Build model with some sample inputs

																					_lowerCAmelCase     :							Optional[Any]    =    tf_tokenizer.get_config()
																					_lowerCAmelCase     :							Dict    =    TFGPTaTokenizer.from_config(lowerCAmelCase__)
																					_lowerCAmelCase     :							Union[str, Any]    =    model_from_config(lowerCAmelCase__)

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







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					# for the test to run
																					_lowerCAmelCase     :							Any    =    12_3123

																					for max_length in [3, 5, 1024]:
																												_lowerCAmelCase     :							Optional[int]    =    tf.convert_to_tensor([self.test_sentences[0]])
																												_lowerCAmelCase     :							List[Any]    =    tf_tokenizer(lowerCAmelCase__,			max_length=lowerCAmelCase__)

																												_lowerCAmelCase     :							Optional[Any]    =    out["input_ids"].numpy().shape[1]

																												assert out_length == max_length




"""simple docstring"""




from typing import TYPE_CHECKING

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


__UpperCamelCase						=      {
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      ['''BlipImageProcessor''']


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlipModel''',
	    '''BlipPreTrainedModel''',
	    '''BlipForConditionalGeneration''',
	    '''BlipForQuestionAnswering''',
	    '''BlipVisionModel''',
	    '''BlipTextModel''',
	    '''BlipForImageTextRetrieval''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFBlipModel''',
	    '''TFBlipPreTrainedModel''',
	    '''TFBlipForConditionalGeneration''',
	    '''TFBlipForQuestionAnswering''',
	    '''TFBlipVisionModel''',
	    '''TFBlipTextModel''',
	    '''TFBlipForImageTextRetrieval''',
	]

if TYPE_CHECKING:
	from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
	from .processing_blip import BlipProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_blip import BlipImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blip import (
		    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlipForConditionalGeneration,
		    BlipForImageTextRetrieval,
		    BlipForQuestionAnswering,
		    BlipModel,
		    BlipPreTrainedModel,
		    BlipTextModel,
		    BlipVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blip import (
		    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFBlipForConditionalGeneration,
		    TFBlipForImageTextRetrieval,
		    TFBlipForQuestionAnswering,
		    TFBlipModel,
		    TFBlipPreTrainedModel,
		    TFBlipTextModel,
		    TFBlipVisionModel,
		)

else:
	import sys

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


