'''simple docstring'''

import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


_lowerCamelCase :				List[Any]														= """1.5.0"""

_lowerCamelCase :				List[Any]														= ["""vision-encoder-decoder"""]



def  _lowerCAmelCase				(				__a      )  ->      Any:
     '''simple docstring'''







     if is_optimum_available():
          from optimum.version import __version__ as optimum_version

          _UpperCamelCase  :Dict       =version.parse(__a      )
          if parsed_optimum_version < version.parse(__a      ):
               raise RuntimeError(
                   F'''transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You '''
                   """can upgrade optimum by running: pip install -U optimum[exporters]"""      )
     else:
          raise RuntimeError(
              """transformers.onnx requires optimum to run, you can install the library by running: pip install """
              """optimum[exporters]"""      )
     _UpperCamelCase  :Tuple       =[
         sys.executable,
         """-m""",
         """optimum.exporters.onnx""",
         F'''--model {args.model}''',
         F'''--task {args.feature}''',
         F'''--framework {args.framework}''' if args.framework is not None else """""",
         F'''{args.output}''',
     ]
     _UpperCamelCase  :Union[str, Any]       =subprocess.Popen(""" """.join(__a      )			,      stdout=subprocess.PIPE			,      shell=__a      )
     proc.wait()

     logger.info(
         """The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as """
         """transformers.onnx is deprecated, and will be removed in v5. You can find more information here: """
         """https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""      )



def  _lowerCAmelCase				(				__a      )  ->      Optional[int]:
     '''simple docstring'''







     _UpperCamelCase  :Optional[int]       =args.output if args.output.is_file() else args.output.joinpath("""model.onnx"""      )
     if not args.output.parent.exists():
          args.output.parent.mkdir(parents=__a      )

     # Allocate the model
     _UpperCamelCase  :Dict       =FeaturesManager.get_model_from_feature(
         args.feature			,      args.model			,      framework=args.framework			,      cache_dir=args.cache_dir      )

     _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =FeaturesManager.check_supported_model_or_raise(__a			,      feature=args.feature      )
     _UpperCamelCase  :int       =model_onnx_config(model.config      )

     if model_kind in ENCODER_DECODER_MODELS:
          _UpperCamelCase  :Tuple       =model.get_encoder()
          _UpperCamelCase  :Tuple       =model.get_decoder()

          _UpperCamelCase  :Dict       =onnx_config.get_encoder_config(encoder_model.config      )
          _UpperCamelCase  :str       =onnx_config.get_decoder_config(
              encoder_model.config			,      decoder_model.config			,      feature=args.feature      )

          if args.opset is None:
               _UpperCamelCase  :int       =max(encoder_onnx_config.default_onnx_opset			,      decoder_onnx_config.default_onnx_opset      )

          if args.opset < min(encoder_onnx_config.default_onnx_opset			,      decoder_onnx_config.default_onnx_opset      ):
               raise ValueError(
                   F'''Opset {args.opset} is not sufficient to export {model_kind}. At least '''
                   F''' {min(encoder_onnx_config.default_onnx_opset			,      decoder_onnx_config.default_onnx_opset      )} is required.'''      )

          _UpperCamelCase  :Optional[Any]       =AutoFeatureExtractor.from_pretrained(args.model      )

          _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =export(
              __a			,      __a			,      __a			,      args.opset			,      args.output.parent.joinpath("""encoder_model.onnx"""      )			,      )

          validate_model_outputs(
              __a			,      __a			,      __a			,      args.output.parent.joinpath("""encoder_model.onnx"""      )			,      __a			,      args.atol if args.atol else encoder_onnx_config.atol_for_validation			,      )

          _UpperCamelCase  :str       =AutoTokenizer.from_pretrained(args.model      )

          _UpperCamelCase       ,      _UpperCamelCase  :str       =export(
              __a			,      __a			,      __a			,      args.opset			,      args.output.parent.joinpath("""decoder_model.onnx"""      )			,      )

          validate_model_outputs(
              __a			,      __a			,      __a			,      args.output.parent.joinpath("""decoder_model.onnx"""      )			,      __a			,      args.atol if args.atol else decoder_onnx_config.atol_for_validation			,      )
          logger.info(
              F'''All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'      ).as_posix()},'''
              F''' {args.output.parent.joinpath('decoder_model.onnx'      ).as_posix()}'''      )

     else:
          # Instantiate the appropriate preprocessor
          if args.preprocessor == "auto":
               _UpperCamelCase  :Union[str, Any]       =get_preprocessor(args.model      )
          elif args.preprocessor == "tokenizer":
               _UpperCamelCase  :Dict       =AutoTokenizer.from_pretrained(args.model      )
          elif args.preprocessor == "image_processor":
               _UpperCamelCase  :Union[str, Any]       =AutoImageProcessor.from_pretrained(args.model      )
          elif args.preprocessor == "feature_extractor":
               _UpperCamelCase  :int       =AutoFeatureExtractor.from_pretrained(args.model      )
          elif args.preprocessor == "processor":
               _UpperCamelCase  :Dict       =AutoProcessor.from_pretrained(args.model      )
          else:
               raise ValueError(F'''Unknown preprocessor type \'{args.preprocessor}\''''      )

          # Ensure the requested opset is sufficient
          if args.opset is None:
               _UpperCamelCase  :Optional[int]       =onnx_config.default_onnx_opset

          if args.opset < onnx_config.default_onnx_opset:
               raise ValueError(
                   F'''Opset {args.opset} is not sufficient to export {model_kind}. '''
                   F'''At least  {onnx_config.default_onnx_opset} is required.'''      )

          _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =export(
              __a			,      __a			,      __a			,      args.opset			,      args.output			,      )

          if args.atol is None:
               _UpperCamelCase  :List[Any]       =onnx_config.atol_for_validation

          validate_model_outputs(__a			,      __a			,      __a			,      args.output			,      __a			,      args.atol      )
          logger.info(F'''All good, model saved at: {args.output.as_posix()}'''      )
          warnings.warn(
              """The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"""
              """ using optimum.exporters.onnx in future. You can find more information here:"""
              """ https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""			,      __a			,      )



def  _lowerCAmelCase				(				)  ->      List[str]:
     '''simple docstring'''







     _UpperCamelCase  :Union[str, Any]       =ArgumentParser("""Hugging Face Transformers ONNX exporter"""      )
     parser.add_argument(
         """-m"""			,      """--model"""			,      type=__a			,      required=__a			,      help="""Model ID on huggingface.co or path on disk to load model from."""      )
     parser.add_argument(
         """--feature"""			,      default="""default"""			,      help="""The type of features to export the model with."""			,      )
     parser.add_argument("""--opset"""			,      type=__a			,      default=__a			,      help="""ONNX opset version to export the model with."""      )
     parser.add_argument(
         """--atol"""			,      type=__a			,      default=__a			,      help="""Absolute difference tolerance when validating the model."""      )
     parser.add_argument(
         """--framework"""			,      type=__a			,      choices=["""pt""", """tf"""]			,      default=__a			,      help=(
             """The framework to use for the ONNX export."""
             """ If not provided, will attempt to use the local checkpoint's original framework"""
             """ or what is available in the environment."""
         )			,      )
     parser.add_argument("""output"""			,      type=__a			,      help="""Path indicating where to store generated ONNX model."""      )
     parser.add_argument("""--cache_dir"""			,      type=__a			,      default=__a			,      help="""Path indicating where to store cache."""      )
     parser.add_argument(
         """--preprocessor"""			,      type=__a			,      choices=["""auto""", """tokenizer""", """feature_extractor""", """image_processor""", """processor"""]			,      default="""auto"""			,      help="""Which type of preprocessor to use. 'auto' tries to automatically detect it."""			,      )
     parser.add_argument(
         """--export_with_transformers"""			,      action="""store_true"""			,      help=(
             """Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be """
             """useful when exporting a model supported in transformers but not in optimum, otherwise it is not """
             """recommended."""
         )			,      )

     _UpperCamelCase  :str       =parser.parse_args()
     if args.export_with_transformers or not is_optimum_available():
          export_with_transformers(__a      )
     else:
          export_with_optimum(__a      )


if __name__ == "__main__":
      _lowerCamelCase :				Optional[int]														= logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
      logger.setLevel(logging.INFO)
      main()
"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.gptj.modeling_tf_gptj import (
						    TFGPTJForCausalLM,
						    TFGPTJForQuestionAnswering,
						    TFGPTJForSequenceClassification,
						    TFGPTJModel,
						    shape_list,
						)



class 			_lowerCamelCase      :







				def __init__(			self     :							Dict      ,       lowerCamelCase_     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     Optional[int]      					=				parent
					_lowercase			:     Tuple      					=				1_3
					_lowercase			:     List[str]      					=				7
					_lowercase			:     int      					=				True
					_lowercase			:     int      					=				True
					_lowercase			:     List[str]      					=				True
					_lowercase			:     Dict      					=				True
					_lowercase			:     Dict      					=				True
					_lowercase			:     Tuple      					=				9_9
					_lowercase			:     List[Any]      					=				3_2
					_lowercase			:     Tuple      					=				4
					_lowercase			:     Dict      					=				2
					_lowercase			:     Optional[Any]      					=				4
					_lowercase			:     List[str]      					=				3_7
					_lowercase			:     int      					=				'gelu'
					_lowercase			:     Dict      					=				0.1
					_lowercase			:     Tuple      					=				0.1
					_lowercase			:     Tuple      					=				5_1_2
					_lowercase			:     Optional[Any]      					=				1_6
					_lowercase			:     str      					=				2
					_lowercase			:     Tuple      					=				0.02
					_lowercase			:     Optional[int]      					=				3
					_lowercase			:     Any      					=				4
					_lowercase			:     Any      					=				None
					_lowercase			:     str      					=				self.vocab_size - 1
					_lowercase			:     str      					=				self.vocab_size - 1
					_lowercase			:     Optional[int]      					=				self.vocab_size - 1







				def 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size    )

					_lowercase			:     Optional[int]      					=				None
					if self.use_input_mask:
						_lowercase			:     str      					=				random_attention_mask([self.batch_size, self.seq_length]    )

					_lowercase			:     Tuple      					=				None
					if self.use_token_type_ids:
						_lowercase			:     Dict      					=				ids_tensor([self.batch_size, self.seq_length]      ,       self.type_vocab_size    )

					_lowercase			:     List[Any]      					=				None
					if self.use_mc_token_ids:
						_lowercase			:     Optional[int]      					=				ids_tensor([self.batch_size, self.num_choices]      ,       self.seq_length    )

					_lowercase			:     List[str]      					=				None
					_lowercase			:     Tuple      					=				None
					_lowercase			:     Any      					=				None
					if self.use_labels:
						_lowercase			:     str      					=				ids_tensor([self.batch_size]      ,       self.type_sequence_label_size    )
						_lowercase			:     List[str]      					=				ids_tensor([self.batch_size, self.seq_length]      ,       self.num_labels    )
						_lowercase			:     str      					=				ids_tensor([self.batch_size]      ,       self.num_choices    )

					_lowercase			:     int      					=				GPTJConfig(
					    vocab_size=self.vocab_size      ,       n_embd=self.hidden_size      ,       n_layer=self.num_hidden_layers      ,       n_head=self.num_attention_heads      ,       intermediate_size=self.intermediate_size      ,       hidden_act=self.hidden_act      ,       hidden_dropout_prob=self.hidden_dropout_prob      ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,       n_positions=self.max_position_embeddings      ,       type_vocab_size=self.type_vocab_size      ,       initializer_range=self.initializer_range      ,       bos_token_id=self.bos_token_id      ,       eos_token_id=self.eos_token_id      ,       pad_token_id=self.pad_token_id      ,       rotary_dim=self.rotary_dim      ,       return_dict=lowerCamelCase_      ,       )

					_lowercase			:     str      					=				ids_tensor([self.num_hidden_layers, self.num_attention_heads]      ,       2    )

					return (
					    config,
					    input_ids,
					    input_mask,
					    head_mask,
					    token_type_ids,
					    mc_token_ids,
					    sequence_labels,
					    token_labels,
					    choice_labels,
					)







				def 				__UpperCAmelCase       (			self     :							Optional[int]      ,       lowerCamelCase_     :							Tuple      ,       lowerCamelCase_     :							int      ,       lowerCamelCase_     :							int      ,       lowerCamelCase_     :							Any      ,       lowerCamelCase_     :							List[str]      ,       *lowerCamelCase_     :							int    ):

					"""simple docstring"""






					_lowercase			:     Dict      					=				TFGPTJModel(config=lowerCamelCase_    )
					_lowercase			:     int      					=				{
					    'input_ids': input_ids,
					    'attention_mask': input_mask,
					    'token_type_ids': token_type_ids,
					}
					_lowercase			:     Optional[Any]      					=				model(lowerCamelCase_    )

					_lowercase			:     Any      					=				[input_ids, None, input_mask]  # None is the input for 'past'
					_lowercase			:     Union[str, Any]      					=				model(lowerCamelCase_    )

					_lowercase			:     Optional[int]      					=				model(lowerCamelCase_    )

					self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)    )







				def 				__UpperCAmelCase       (			self     :							Optional[Any]      ,       lowerCamelCase_     :							List[Any]      ,       lowerCamelCase_     :							List[Any]      ,       lowerCamelCase_     :							Optional[Any]      ,       lowerCamelCase_     :							Tuple      ,       lowerCamelCase_     :							Union[str, Any]      ,       *lowerCamelCase_     :							int    ):

					"""simple docstring"""






					_lowercase			:     Union[str, Any]      					=				TFGPTJModel(config=lowerCamelCase_    )

					# first forward pass
					_lowercase			:     str      					=				model(lowerCamelCase_      ,       token_type_ids=lowerCamelCase_      ,       use_cache=lowerCamelCase_    )
					_lowercase			:     List[Any]      					=				model(lowerCamelCase_      ,       token_type_ids=lowerCamelCase_    )
					_lowercase			:     int      					=				model(lowerCamelCase_      ,       token_type_ids=lowerCamelCase_      ,       use_cache=lowerCamelCase_    )

					self.parent.assertTrue(len(lowerCamelCase_    ) == len(lowerCamelCase_    )    )
					self.parent.assertTrue(len(lowerCamelCase_    ) == len(lowerCamelCase_    ) + 1    )

					_lowercase					,   _lowercase			:     Any      					=				outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					_lowercase			:     Optional[int]      					=				ids_tensor((self.batch_size, 1)      ,       config.vocab_size    )
					_lowercase			:     Any      					=				ids_tensor([self.batch_size, 1]      ,       self.type_vocab_size    )

					# append to next input_ids and token_type_ids
					_lowercase			:     Optional[Any]      					=				tf.concat([input_ids, next_tokens]      ,       axis=-1    )
					_lowercase			:     Dict      					=				tf.concat([token_type_ids, next_token_types]      ,       axis=-1    )

					_lowercase			:     List[Any]      					=				model(lowerCamelCase_      ,       token_type_ids=lowerCamelCase_    )['last_hidden_state']
					_lowercase			:     Dict      					=				model(lowerCamelCase_      ,       token_type_ids=lowerCamelCase_      ,       past_key_values=lowerCamelCase_    )[
					    'last_hidden_state'
					]

					# select random slice
					_lowercase			:     Dict      					=				int(ids_tensor((1,)      ,       shape_list(lowerCamelCase_    )[-1]    )    )
					_lowercase			:     Optional[Any]      					=				output_from_no_past[:, -1, random_slice_idx]
					_lowercase			:     Tuple      					=				output_from_past[:, 0, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(lowerCamelCase_      ,       lowerCamelCase_      ,       rtol=1E-6    )







				def 				__UpperCAmelCase       (			self     :							Tuple      ,       lowerCamelCase_     :							Optional[int]      ,       lowerCamelCase_     :							List[str]      ,       lowerCamelCase_     :							int      ,       lowerCamelCase_     :							int      ,       lowerCamelCase_     :							Optional[int]      ,       *lowerCamelCase_     :							str    ):

					"""simple docstring"""






					_lowercase			:     Tuple      					=				TFGPTJModel(config=lowerCamelCase_    )

					# create attention mask
					_lowercase			:     List[str]      					=				self.seq_length // 2
					_lowercase			:     Any      					=				tf.ones((self.batch_size, half_seq_length)      ,       dtype=tf.intaa    )
					_lowercase			:     Optional[Any]      					=				tf.zeros((self.batch_size, self.seq_length - half_seq_length)      ,       dtype=tf.intaa    )
					_lowercase			:     int      					=				tf.concat([attn_mask_begin, attn_mask_end]      ,       axis=1    )

					# first forward pass
					_lowercase					,   _lowercase			:     str      					=				model(lowerCamelCase_      ,       attention_mask=lowerCamelCase_    ).to_tuple()

					# create hypothetical next token and extent to next_input_ids
					_lowercase			:     Tuple      					=				ids_tensor((self.batch_size, 1)      ,       config.vocab_size    )

					# change a random masked slice from input_ids
					_lowercase			:     int      					=				ids_tensor((1,)      ,       lowerCamelCase_    ).numpy() + 1
					_lowercase			:     Dict      					=				ids_tensor((self.batch_size, self.seq_length)      ,       config.vocab_size    )
					_lowercase			:     Tuple      					=				tf.range(self.seq_length    ) == (self.seq_length - random_seq_idx_to_change)
					_lowercase			:     List[str]      					=				tf.transpose(
					    tf.broadcast_to(tf.expand_dims(lowerCamelCase_      ,       -1    )      ,       (self.seq_length, self.batch_size)    )    )
					_lowercase			:     Tuple      					=				tf.where(lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_    )

					# append to next input_ids and attn_mask
					_lowercase			:     Tuple      					=				tf.concat([input_ids, next_tokens]      ,       axis=-1    )
					_lowercase			:     Union[str, Any]      					=				tf.concat([attn_mask, tf.ones((shape_list(lowerCamelCase_    )[0], 1)      ,       dtype=tf.intaa    )]      ,       axis=1    )

					# get two different outputs
					_lowercase			:     str      					=				model(lowerCamelCase_      ,       attention_mask=lowerCamelCase_    )['last_hidden_state']
					_lowercase			:     List[str]      					=				model(lowerCamelCase_      ,       past_key_values=lowerCamelCase_      ,       attention_mask=lowerCamelCase_    )[
					    'last_hidden_state'
					]

					# select random slice
					_lowercase			:     Any      					=				int(ids_tensor((1,)      ,       shape_list(lowerCamelCase_    )[-1]    )    )
					_lowercase			:     int      					=				output_from_no_past[:, -1, random_slice_idx]
					_lowercase			:     Any      					=				output_from_past[:, 0, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(lowerCamelCase_      ,       lowerCamelCase_      ,       rtol=1E-12    )







				def 				__UpperCAmelCase       (			self     :							Optional[int]      ,       lowerCamelCase_     :							int      ,       lowerCamelCase_     :							str      ,       lowerCamelCase_     :							Union[str, Any]      ,       lowerCamelCase_     :							Union[str, Any]      ,       lowerCamelCase_     :							Optional[int]      ,       *lowerCamelCase_     :							int    ):

					"""simple docstring"""






					_lowercase			:     Tuple      					=				TFGPTJModel(config=lowerCamelCase_    )

					_lowercase			:     Dict      					=				input_ids[:1, :]
					_lowercase			:     List[str]      					=				input_mask[:1, :]
					_lowercase			:     Optional[Any]      					=				token_type_ids[:1, :]
					_lowercase			:     int      					=				1

					# first forward pass
					_lowercase			:     int      					=				model(lowerCamelCase_      ,       attention_mask=lowerCamelCase_      ,       token_type_ids=lowerCamelCase_      ,       use_cache=lowerCamelCase_    )

					_lowercase					,   _lowercase			:     Any      					=				outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					_lowercase			:     Union[str, Any]      					=				ids_tensor((self.batch_size, 3)      ,       config.vocab_size    )
					_lowercase			:     Union[str, Any]      					=				ids_tensor((self.batch_size, 3)      ,       2    )
					_lowercase			:     Any      					=				ids_tensor((self.batch_size, 3)      ,       self.type_vocab_size    )

					# append to next input_ids and token_type_ids
					_lowercase			:     List[Any]      					=				tf.concat([input_ids, next_tokens]      ,       axis=-1    )
					_lowercase			:     Any      					=				tf.concat([input_mask, next_attn_mask]      ,       axis=-1    )
					_lowercase			:     List[Any]      					=				tf.concat([token_type_ids, next_token_types]      ,       axis=-1    )

					_lowercase			:     str      					=				model(
					    lowerCamelCase_      ,       token_type_ids=lowerCamelCase_      ,       attention_mask=lowerCamelCase_    )['last_hidden_state']
					_lowercase			:     List[Any]      					=				model(
					    lowerCamelCase_      ,       token_type_ids=lowerCamelCase_      ,       attention_mask=lowerCamelCase_      ,       past_key_values=lowerCamelCase_      ,       )['last_hidden_state']
					self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]    )

					# select random slice
					_lowercase			:     Tuple      					=				int(ids_tensor((1,)      ,       shape_list(lowerCamelCase_    )[-1]    )    )
					_lowercase			:     List[str]      					=				output_from_no_past[:, -3:, random_slice_idx]
					_lowercase			:     Optional[Any]      					=				output_from_past[:, :, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(lowerCamelCase_      ,       lowerCamelCase_      ,       rtol=1E-3    )







				def 				__UpperCAmelCase       (			self     :							List[Any]      ,       lowerCamelCase_     :							Optional[Any]      ,       lowerCamelCase_     :							Optional[Any]      ,       lowerCamelCase_     :							str      ,       lowerCamelCase_     :							int      ,       lowerCamelCase_     :							Dict      ,       *lowerCamelCase_     :							List[Any]    ):

					"""simple docstring"""






					_lowercase			:     Tuple      					=				TFGPTJForCausalLM(config=lowerCamelCase_    )
					_lowercase			:     Optional[int]      					=				{
					    'input_ids': input_ids,
					    'attention_mask': input_mask,
					    'token_type_ids': token_type_ids,
					}
					_lowercase			:     Optional[int]      					=				model(lowerCamelCase_    )
					self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)    )







				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					_lowercase			:     int      					=				self.prepare_config_and_inputs()

					(
					    (
					    _lowercase
					)					,   (
					    _lowercase
					)					,   (
					    _lowercase
					)					,   (
					    _lowercase
					)					,   (
					    _lowercase
					)					,   (
					    _lowercase
					)					,   (
					    _lowercase
					)					,   (
					    _lowercase
					)					,   (
					    _lowercase
					)					,   
					)			:     str      					=				config_and_inputs

					_lowercase			:     Tuple      					=				{
					    'input_ids': input_ids,
					    'token_type_ids': token_type_ids,
					    'attention_mask': input_mask,
					}
					return config, inputs_dict



@require_tf
class 			_lowerCamelCase      (__lowerCamelCase    ,				__lowerCamelCase    ,				__lowerCamelCase    ,				unittest.TestCase  ):
				_snake_case  =	(
				    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
				    if is_tf_available()
				    else ()
				)

				_snake_case  =	(TFGPTJForCausalLM,) if is_tf_available() else ()
				_snake_case  =	(
				    {
				        "feature-extraction": TFGPTJModel,
				        "question-answering": TFGPTJForQuestionAnswering,
				        "text-classification": TFGPTJForSequenceClassification,
				        "text-generation": TFGPTJForCausalLM,
				        "zero-shot": TFGPTJForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				_snake_case  =	False
				_snake_case  =	False
				_snake_case  =	False
				_snake_case  =	False







				def 				__UpperCAmelCase       (			self     :							Optional[Any]      ,       lowerCamelCase_     :							Union[str, Any]      ,       lowerCamelCase_     :							Any      ,       lowerCamelCase_     :							Optional[int]      ,       lowerCamelCase_     :							str      ,       lowerCamelCase_     :							str    ):

					"""simple docstring"""






					if (
					    pipeline_test_casse_name == "QAPipelineTests"
					    and tokenizer_name is not None
					    and not tokenizer_name.endswith('Fast'    )
					):
						# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
						# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
						# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
						return True

					return False







				def 				__UpperCAmelCase       (			self     :							str    ):

					"""simple docstring"""






					_lowercase			:     str      					=				TFGPTJModelTester(self    )
					_lowercase			:     List[Any]      					=				ConfigTester(self      ,       config_class=lowerCamelCase_      ,       n_embd=3_7    )







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""simple docstring"""






					self.config_tester.run_common_tests()







				def 				__UpperCAmelCase       (			self     :							Optional[Any]    ):

					"""simple docstring"""






					_lowercase			:     Union[str, Any]      					=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model(*lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     str      					=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_past(*lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					_lowercase			:     str      					=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_attention_mask_past(*lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					_lowercase			:     str      					=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_past_large_inputs(*lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""simple docstring"""






					_lowercase			:     Union[str, Any]      					=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_lm_head_model(*lowerCamelCase_    )







				@slow
				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices('GPU'    )    ) > 0      ,       'skip testing on GPU for now to avoid GPU OOM.'      ,       )
				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					_lowercase			:     Optional[int]      					=				TFGPTJModel.from_pretrained('EleutherAI/gpt-j-6B'      ,       from_pt=lowerCamelCase_    )
					self.assertIsNotNone(lowerCamelCase_    )







				@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'    )
				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					super().test_resize_token_embeddings()



@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 			_lowerCamelCase      (unittest.TestCase  ):







				def 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






					_lowercase			:     int      					=				TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      ,       from_pt=lowerCamelCase_    )
					_lowercase			:     Any      					=				tf.convert_to_tensor([[4_6_4, 3_2_9_0]]      ,       dtype=tf.intaa    )  # The dog
					# fmt: off
					# The dog is a man's best friend. It is a loyal companion, and it is a friend
					_lowercase			:     Any      					=				[4_6_4, 3_2_9_0, 3_1_8, 2_5_7, 5_8_2, 3_3_8, 1_2_6_6, 1_5_4_5, 1_3, 6_3_2, 3_1_8, 2_5_7, 9_1_1_2, 1_5_1_8_5, 1_1, 2_9_0, 3_4_0, 3_1_8, 2_5_7, 1_5_4_5]
					# fmt: on
					_lowercase			:     Any      					=				model.generate(lowerCamelCase_      ,       do_sample=lowerCamelCase_    )
					self.assertListEqual(output_ids[0].numpy().tolist()      ,       lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							str    ):

					"""simple docstring"""






					_lowercase			:     int      					=				AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B'      ,       revision='float16'    )
					_lowercase			:     int      					=				TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      ,       revision='float16'      ,       from_pt=lowerCamelCase_    )

					_lowercase			:     List[Any]      					=				tokenizer('Today is a nice day and'      ,       return_tensors='tf'    )
					# forces the generation to happen on CPU, to avoid GPU-related quirks
					with tf.device(':/CPU:0'    ):
						_lowercase			:     str      					=				model.generate(**lowerCamelCase_      ,       do_sample=lowerCamelCase_      ,       seed=[4_2, 0]    )
					_lowercase			:     Optional[int]      					=				tokenizer.decode(output_ids[0]      ,       skip_special_tokens=lowerCamelCase_    )

					_lowercase			:     Tuple      					=				'Today is a nice day and I’m going to go for a walk. I’'
					self.assertEqual(lowerCamelCase_      ,       lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      ,       revision='float16'      ,       from_pt=lowerCamelCase_    )
					_lowercase			:     Dict      					=				AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B'      ,       revision='float16'    )

					_lowercase			:     List[str]      					=				'left'

					# Define PAD Token = EOS Token = 50256
					_lowercase			:     List[str]      					=				tokenizer.eos_token
					_lowercase			:     Any      					=				model.config.eos_token_id

					# use different length sentences to test batching
					_lowercase			:     Optional[Any]      					=				[
					    'Hello, my dog is a little',
					    'Today, I',
					]
					_lowercase			:     List[str]      					=				[
					    'Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia',
					    'Today, I’m going to be talking about a topic that’',
					]
					return model, tokenizer, sentences, expected_output_sentences







				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					_lowercase					,   _lowercase					,   _lowercase					,   _lowercase			:     List[str]      					=				self._get_beam_search_test_objects()

					_lowercase			:     Optional[Any]      					=				tokenizer(lowerCamelCase_      ,       return_tensors='tf'      ,       padding=lowerCamelCase_    )
					_lowercase			:     Optional[Any]      					=				model.generate(**lowerCamelCase_      ,       do_sample=lowerCamelCase_      ,       num_beams=2    )
					_lowercase			:     List[Any]      					=				tokenizer.batch_decode(lowerCamelCase_      ,       skip_special_tokens=lowerCamelCase_    )
					self.assertListEqual(lowerCamelCase_      ,       lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Optional[Any]    ):

					"""simple docstring"""






					_lowercase					,   _lowercase					,   _lowercase					,   _lowercase			:     List[Any]      					=				self._get_beam_search_test_objects()

					_lowercase			:     str      					=				tokenizer(lowerCamelCase_      ,       return_tensors='tf'      ,       padding=lowerCamelCase_    )
					_lowercase			:     Tuple      					=				tokenizer(sentences[0]      ,       return_tensors='tf'    )
					_lowercase			:     str      					=				model.generate(**lowerCamelCase_      ,       do_sample=lowerCamelCase_      ,       num_beams=2    )
					_lowercase			:     List[Any]      					=				(
					    shape_list(inputs_non_padded['input_ids']    )[-1]
					    - tf.reduce_sum(tf.cast(inputs['attention_mask'][-1]      ,       tf.intaa    )    ).numpy()
					)
					_lowercase			:     Dict      					=				tokenizer(sentences[1]      ,       return_tensors='tf'    )
					_lowercase			:     Optional[Any]      					=				model.generate(
					    **lowerCamelCase_      ,       do_sample=lowerCamelCase_      ,       num_beams=2      ,       max_length=model.config.max_length - num_paddings    )
					_lowercase			:     Tuple      					=				tokenizer.decode(output_non_padded[0]      ,       skip_special_tokens=lowerCamelCase_    )
					_lowercase			:     List[str]      					=				tokenizer.decode(output_padded[0]      ,       skip_special_tokens=lowerCamelCase_    )
					self.assertListEqual(lowerCamelCase_      ,       [non_padded_sentence, padded_sentence]    )







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase					,   _lowercase					,   _lowercase					,   _lowercase			:     int      					=				self._get_beam_search_test_objects()

					_lowercase			:     Tuple      					=				tokenizer(lowerCamelCase_      ,       return_tensors='tf'      ,       padding=lowerCamelCase_    )
					_lowercase			:     Optional[int]      					=				tf.function(model.generate      ,       jit_compile=lowerCamelCase_    )
					_lowercase			:     Any      					=				xla_generate(**lowerCamelCase_      ,       do_sample=lowerCamelCase_      ,       num_beams=2    )
					_lowercase			:     int      					=				tokenizer.batch_decode(lowerCamelCase_      ,       skip_special_tokens=lowerCamelCase_    )
					self.assertListEqual(lowerCamelCase_      ,       lowerCamelCase_    )



