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            =		["""gpt2"""]
_UpperCAmelCase            =		"""gpt2"""

if is_tf_available():





	class UpperCAmelCase						( tf.Module  ):


					'''simple docstring'''






					def __init__(						self		,     lowercase ):



							"""simple docstring"""





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







					@tf.function(input_signature=(tf.TensorSpec((None,)		,     tf.string		,     name='text' ),) )
					def   lowerCAmelCase_       (						self		,     lowercase ):



							"""simple docstring"""





							A_		:    List[str]         =				self.tokenizer(lowercase )
							A_		:    Tuple         =				tokenized['input_ids'].to_tensor()

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

							A_		:    List[Any]         =				self.model(input_ids=lowercase		,     attention_mask=lowercase )['logits']

							return outputs





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


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						super().setUp()

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

						A_		:    Union[str, 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',
						]
						A_		:    Tuple         =				list(zip(self.test_sentences		,     self.test_sentences[::-1] ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for tokenizer, tf_tokenizer in zip(self.tokenizers		,     self.tf_tokenizers ):
								for test_inputs in self.test_sentences:
										A_		:    List[Any]         =				tokenizer([test_inputs]		,     return_tensors='tf' )
										A_		:    str         =				tf_tokenizer([test_inputs] )

										for key in python_outputs.keys():
												# convert them to numpy to avoid messing with ragged tensors
												A_		:    Optional[int]         =				python_outputs[key].numpy()
												A_		:    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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for tf_tokenizer in self.tf_tokenizers:
								A_		:    Any         =				tf.function(lowercase )
								for test_inputs in self.test_sentences:
										A_		:    Optional[int]         =				tf.constant(lowercase )
										A_		:    Tuple         =				compiled_tokenizer(lowercase )
										A_		:    Tuple         =				tf_tokenizer(lowercase )

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



						"""simple docstring"""





						for tf_tokenizer in self.tf_tokenizers:
								A_		:    Any         =				ModelToSave(tokenizer=lowercase )
								A_		:    int         =				tf.convert_to_tensor([self.test_sentences[0]] )
								A_		:    Dict         =				model.serving(lowercase )  # Build model with some sample inputs
								with TemporaryDirectory() as tempdir:
										A_		:    Dict         =				Path(lowercase ) / 'saved.model'
										tf.saved_model.save(lowercase		,     lowercase		,     signatures={'serving_default': model.serving} )
										A_		:    List[Any]         =				tf.saved_model.load(lowercase )
								A_		:    List[str]         =				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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for tf_tokenizer in self.tf_tokenizers:
								A_		:    Dict         =				tf.convert_to_tensor([self.test_sentences[0]] )
								A_		:    List[str]         =				tf_tokenizer(lowercase )  # Build model with some sample inputs

								A_		:    Any         =				tf_tokenizer.get_config()
								A_		:    str         =				TFGPTaTokenizer.from_config(lowercase )
								A_		:    Any         =				model_from_config(lowercase )

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







				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for tf_tokenizer in self.tf_tokenizers:
								# for the test to run
								A_		:    Dict         =				1_2_3_1_2_3

								for max_length in [3, 5, 1_0_2_4]:
										A_		:    Optional[int]         =				tf.convert_to_tensor([self.test_sentences[0]] )
										A_		:    Any         =				tf_tokenizer(lowercase		,     max_length=lowercase )

										A_		:    Union[str, Any]         =				out['input_ids'].numpy().shape[1]

										assert out_length == max_length







import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
	import onnxruntime as ort


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """tensor(bool)""": np.bool_,
    """tensor(int8)""": np.inta,
    """tensor(uint8)""": np.uinta,
    """tensor(int16)""": np.intaa,
    """tensor(uint16)""": np.uintaa,
    """tensor(int32)""": np.intaa,
    """tensor(uint32)""": np.uintaa,
    """tensor(int64)""": np.intaa,
    """tensor(uint64)""": np.uintaa,
    """tensor(float16)""": np.floataa,
    """tensor(float)""": np.floataa,
    """tensor(double)""": np.floataa,
}





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self		,     lowercase=None		,     **lowercase ):



						"""simple docstring"""





						logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
						A_		:    List[Any]         =				model
						A_		:    Dict         =				kwargs.get('model_save_dir'		,     lowercase )
						A_		:    List[str]         =				kwargs.get('latest_model_name'		,     lowercase )
				def __call__(						self		,     **lowercase ):



						"""simple docstring"""





						A_		:    str         =				{k: np.array(lowercase ) for k, v in kwargs.items()}
						return self.model.run(lowercase		,     lowercase )
				@staticmethod
				def   lowerCAmelCase_       (						lowercase		,     lowercase=None		,     lowercase=None ):



						"""simple docstring"""





						if provider is None:
								logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
								A_		:    List[Any]         =				'CPUExecutionProvider'

						return ort.InferenceSession(lowercase		,     providers=[provider]		,     sess_options=lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     **lowercase ):



						"""simple docstring"""





						A_		:    str         =				file_name if file_name is not None else ONNX_WEIGHTS_NAME

						A_		:    Optional[int]         =				self.model_save_dir.joinpath(self.latest_model_name )
						A_		:    int         =				Path(lowercase ).joinpath(lowercase )
						try:
								shutil.copyfile(lowercase		,     lowercase )
						except shutil.SameFileError:
								pass

						# copy external weights (for models >2GB)
						A_		:    Optional[Any]         =				self.model_save_dir.joinpath(lowercase )
						if src_path.exists():
								A_		:    int         =				Path(lowercase ).joinpath(lowercase )
								try:
										shutil.copyfile(lowercase		,     lowercase )
								except shutil.SameFileError:
										pass
				def   lowerCAmelCase_       (						self		,     lowercase		,     **lowercase		,     ):



						"""simple docstring"""





						if os.path.isfile(lowercase ):
								logger.error(F'''Provided path ({save_directory}) should be a directory, not a file''' )
								return

						os.makedirs(lowercase		,     exist_ok=lowercase )

						# saving model weights/files
						self._save_pretrained(lowercase		,     **lowercase )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     lowercase = None		,     lowercase = None		,     lowercase = False		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Any         =				file_name if file_name is not None else ONNX_WEIGHTS_NAME
						# load model from local directory
						if os.path.isdir(lowercase ):
								A_		:    Optional[int]         =				OnnxRuntimeModel.load_model(
								    os.path.join(lowercase		,     lowercase )		,     provider=lowercase		,     sess_options=lowercase )
								A_		:    Dict         =				Path(lowercase )
						# load model from hub
						else:
								# download model
								A_		:    List[str]         =				hf_hub_download(
								    repo_id=lowercase		,     filename=lowercase		,     use_auth_token=lowercase		,     revision=lowercase		,     cache_dir=lowercase		,     force_download=lowercase		,     )
								A_		:    int         =				Path(lowercase ).parent
								A_		:    Optional[Any]         =				Path(lowercase ).name
								A_		:    Any         =				OnnxRuntimeModel.load_model(lowercase		,     provider=lowercase		,     sess_options=lowercase )
						return cls(model=lowercase		,     **lowercase )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     lowercase = True		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[Any]         =				None
						if len(str(lowercase ).split('@' ) ) == 2:
								A_       ,      A_		:    int         =				model_id.split('@' )

						return cls._from_pretrained(
						    model_id=lowercase		,     revision=lowercase		,     cache_dir=lowercase		,     force_download=lowercase		,     use_auth_token=lowercase		,     **lowercase		,     )






