





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 						UpperCamelCase__					:



     '''simple docstring'''

     def __init__(				self		:       List[str]		,lowerCamelCase__		:       List[str]			)	->						Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		parent
           SCREAMING_SNAKE_CASE						=		13
           SCREAMING_SNAKE_CASE						=		7
           SCREAMING_SNAKE_CASE						=		True
           SCREAMING_SNAKE_CASE						=		True
           SCREAMING_SNAKE_CASE						=		True
           SCREAMING_SNAKE_CASE						=		True
           SCREAMING_SNAKE_CASE						=		True
           SCREAMING_SNAKE_CASE						=		99
           SCREAMING_SNAKE_CASE						=		32
           SCREAMING_SNAKE_CASE						=		4
           SCREAMING_SNAKE_CASE						=		2
           SCREAMING_SNAKE_CASE						=		4
           SCREAMING_SNAKE_CASE						=		37
           SCREAMING_SNAKE_CASE						=		'''gelu'''
           SCREAMING_SNAKE_CASE						=		0.1
           SCREAMING_SNAKE_CASE						=		0.1
           SCREAMING_SNAKE_CASE						=		512
           SCREAMING_SNAKE_CASE						=		16
           SCREAMING_SNAKE_CASE						=		2
           SCREAMING_SNAKE_CASE						=		0.02
           SCREAMING_SNAKE_CASE						=		3
           SCREAMING_SNAKE_CASE						=		4
           SCREAMING_SNAKE_CASE						=		None
           SCREAMING_SNAKE_CASE						=		self.vocab_size - 1
           SCREAMING_SNAKE_CASE						=		self.vocab_size - 1
           SCREAMING_SNAKE_CASE						=		self.vocab_size - 1






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size			)

           SCREAMING_SNAKE_CASE						=		None
           if self.use_input_mask:
                 SCREAMING_SNAKE_CASE						=		random_attention_mask([self.batch_size, self.seq_length]			)

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

           SCREAMING_SNAKE_CASE						=		None
           if self.use_mc_token_ids:
                 SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size, self.num_choices]		,self.seq_length			)

           SCREAMING_SNAKE_CASE						=		None
           SCREAMING_SNAKE_CASE						=		None
           SCREAMING_SNAKE_CASE						=		None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size]		,self.type_sequence_label_size			)
                 SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size, self.seq_length]		,self.num_labels			)
                 SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size]		,self.num_choices			)

           SCREAMING_SNAKE_CASE						=		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		,)

           SCREAMING_SNAKE_CASE						=		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 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       int		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       List[Any]		,*lowerCamelCase__		:       Tuple			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		TFGPTJModel(config=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }
           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase			)

           SCREAMING_SNAKE_CASE						=		[input_ids, None, input_mask]  # None is the input for 'past'
           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase			)

           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase			)

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






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       int		,lowerCamelCase__		:       Any		,lowerCamelCase__		:       List[Any]		,lowerCamelCase__		:       Optional[int]		,*lowerCamelCase__		:       Optional[Any]			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		TFGPTJModel(config=_lowerCamelCase			)

           # first forward pass
           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase		,token_type_ids=_lowerCamelCase		,use_cache=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase		,token_type_ids=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase		,token_type_ids=_lowerCamelCase		,use_cache=_lowerCamelCase			)

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

           SCREAMING_SNAKE_CASE						=		outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           SCREAMING_SNAKE_CASE						=		ids_tensor((self.batch_size, 1)		,config.vocab_size			)
           SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size, 1]		,self.type_vocab_size			)

           # append to next input_ids and token_type_ids
           SCREAMING_SNAKE_CASE						=		tf.concat([input_ids, next_tokens]		,axis=-1			)
           SCREAMING_SNAKE_CASE						=		tf.concat([token_type_ids, next_token_types]		,axis=-1			)

           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase		,token_type_ids=_lowerCamelCase			)['''last_hidden_state''']
           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase		,token_type_ids=_lowerCamelCase		,past_key_values=_lowerCamelCase			)[
               '''last_hidden_state'''
           ]

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

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






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       Any		,*lowerCamelCase__		:       Union[str, Any]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		TFGPTJModel(config=_lowerCamelCase			)

           # create attention mask
           SCREAMING_SNAKE_CASE						=		self.seq_length // 2
           SCREAMING_SNAKE_CASE						=		tf.ones((self.batch_size, half_seq_length)		,dtype=tf.intaa			)
           SCREAMING_SNAKE_CASE						=		tf.zeros((self.batch_size, self.seq_length - half_seq_length)		,dtype=tf.intaa			)
           SCREAMING_SNAKE_CASE						=		tf.concat([attn_mask_begin, attn_mask_end]		,axis=1			)

           # first forward pass
           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase		,attention_mask=_lowerCamelCase			).to_tuple()

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

           # change a random masked slice from input_ids
           SCREAMING_SNAKE_CASE						=		ids_tensor((1,)		,_lowerCamelCase			).numpy() + 1
           SCREAMING_SNAKE_CASE						=		ids_tensor((self.batch_size, self.seq_length)		,config.vocab_size			)
           SCREAMING_SNAKE_CASE						=		tf.range(self.seq_length			) == (self.seq_length - random_seq_idx_to_change)
           SCREAMING_SNAKE_CASE						=		tf.transpose(
               tf.broadcast_to(tf.expand_dims(_lowerCamelCase		,-1			)		,(self.seq_length, self.batch_size)			)			)
           SCREAMING_SNAKE_CASE						=		tf.where(_lowerCamelCase		,_lowerCamelCase		,_lowerCamelCase			)

           # append to next input_ids and attn_mask
           SCREAMING_SNAKE_CASE						=		tf.concat([input_ids, next_tokens]		,axis=-1			)
           SCREAMING_SNAKE_CASE						=		tf.concat([attn_mask, tf.ones((shape_list(_lowerCamelCase			)[0], 1)		,dtype=tf.intaa			)]		,axis=1			)

           # get two different outputs
           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase		,attention_mask=_lowerCamelCase			)['''last_hidden_state''']
           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase		,past_key_values=_lowerCamelCase		,attention_mask=_lowerCamelCase			)[
               '''last_hidden_state'''
           ]

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

           # test that outputs are equal for slice
           tf.debugging.assert_near(_lowerCamelCase		,_lowerCamelCase		,rtol=1e-1_2			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       int		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       List[Any]		,*lowerCamelCase__		:       Tuple			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		TFGPTJModel(config=_lowerCamelCase			)

           SCREAMING_SNAKE_CASE						=		input_ids[:1, :]
           SCREAMING_SNAKE_CASE						=		input_mask[:1, :]
           SCREAMING_SNAKE_CASE						=		token_type_ids[:1, :]
           SCREAMING_SNAKE_CASE						=		1

           # first forward pass
           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase		,attention_mask=_lowerCamelCase		,token_type_ids=_lowerCamelCase		,use_cache=_lowerCamelCase			)

           SCREAMING_SNAKE_CASE						=		outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           SCREAMING_SNAKE_CASE						=		ids_tensor((self.batch_size, 3)		,config.vocab_size			)
           SCREAMING_SNAKE_CASE						=		ids_tensor((self.batch_size, 3)		,2			)
           SCREAMING_SNAKE_CASE						=		ids_tensor((self.batch_size, 3)		,self.type_vocab_size			)

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

           SCREAMING_SNAKE_CASE						=		model(
               _lowerCamelCase		,token_type_ids=_lowerCamelCase		,attention_mask=_lowerCamelCase			)['''last_hidden_state''']
           SCREAMING_SNAKE_CASE						=		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
           SCREAMING_SNAKE_CASE						=		int(ids_tensor((1,)		,shape_list(_lowerCamelCase			)[-1]			)			)
           SCREAMING_SNAKE_CASE						=		output_from_no_past[:, -3:, random_slice_idx]
           SCREAMING_SNAKE_CASE						=		output_from_past[:, :, random_slice_idx]

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






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       Union[str, Any]		,lowerCamelCase__		:       Any		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       Tuple		,*lowerCamelCase__		:       Optional[Any]			)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		TFGPTJForCausalLM(config=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }
           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.vocab_size)			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.prepare_config_and_inputs()

           (
               SCREAMING_SNAKE_CASE			
           )						=		config_and_inputs

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

@require_tf
class 						UpperCamelCase__					( __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, unittest.TestCase  ):



     '''simple docstring'''

     __snake_case       :      Dict						      =     (
         (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
         if is_tf_available()
         else ()
     )

     __snake_case       :      Optional[int]						      =     (TFGPTJForCausalLM,) if is_tf_available() else ()
     __snake_case       :      int						      =     (
         {
             """feature-extraction""": TFGPTJModel,
             """question-answering""": TFGPTJForQuestionAnswering,
             """text-classification""": TFGPTJForSequenceClassification,
             """text-generation""": TFGPTJForCausalLM,
             """zero-shot""": TFGPTJForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     __snake_case       :      Tuple						      =     False
     __snake_case       :      Optional[int]						      =     False
     __snake_case       :      Dict						      =     False
     __snake_case       :      Optional[int]						      =     False






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str		,lowerCamelCase__		:       List[Any]		,lowerCamelCase__		:       Any		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       str		,lowerCamelCase__		:       Union[str, Any]			)	->						List[Any]:




           '''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 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		TFGPTJModelTester(self			)
           SCREAMING_SNAKE_CASE						=		ConfigTester(self		,config_class=_lowerCamelCase		,n_embd=37			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						Any:




           '''simple docstring'''


           self.config_tester.run_common_tests()






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]			)	->						Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_gptj_model(*_lowerCamelCase			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_gptj_model_past(*_lowerCamelCase			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_gptj_model_attention_mask_past(*_lowerCamelCase			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple			)	->						List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_gptj_model_past_large_inputs(*_lowerCamelCase			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		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 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		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 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						str:




           '''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 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""		,from_pt=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		tf.convert_to_tensor([[464, 3290]]		,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
           SCREAMING_SNAKE_CASE						=		[464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
           # fmt: on
           SCREAMING_SNAKE_CASE						=		model.generate(_lowerCamelCase		,do_sample=_lowerCamelCase			)
           self.assertListEqual(output_ids[0].numpy().tolist()		,_lowerCamelCase			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""		,revision="""float16"""			)
           SCREAMING_SNAKE_CASE						=		TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""		,revision="""float16"""		,from_pt=_lowerCamelCase			)

           SCREAMING_SNAKE_CASE						=		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"""			):
                 SCREAMING_SNAKE_CASE						=		model.generate(**_lowerCamelCase		,do_sample=_lowerCamelCase		,seed=[42, 0]			)
           SCREAMING_SNAKE_CASE						=		tokenizer.decode(output_ids[0]		,skip_special_tokens=_lowerCamelCase			)

           SCREAMING_SNAKE_CASE						=		'''Today is a nice day and I’m going to go for a walk. I’'''
           self.assertEqual(_lowerCamelCase		,_lowerCamelCase			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""		,revision="""float16"""		,from_pt=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""		,revision="""float16"""			)

           SCREAMING_SNAKE_CASE						=		'''left'''

           # Define PAD Token = EOS Token = 50256
           SCREAMING_SNAKE_CASE						=		tokenizer.eos_token
           SCREAMING_SNAKE_CASE						=		model.config.eos_token_id

           # use different length sentences to test batching
           SCREAMING_SNAKE_CASE						=		[
               '''Hello, my dog is a little''',
               '''Today, I''',
           ]
           SCREAMING_SNAKE_CASE						=		[
               '''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 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self._get_beam_search_test_objects()

           SCREAMING_SNAKE_CASE						=		tokenizer(_lowerCamelCase		,return_tensors="""tf"""		,padding=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		model.generate(**_lowerCamelCase		,do_sample=_lowerCamelCase		,num_beams=2			)
           SCREAMING_SNAKE_CASE						=		tokenizer.batch_decode(_lowerCamelCase		,skip_special_tokens=_lowerCamelCase			)
           self.assertListEqual(_lowerCamelCase		,_lowerCamelCase			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self._get_beam_search_test_objects()

           SCREAMING_SNAKE_CASE						=		tokenizer(_lowerCamelCase		,return_tensors="""tf"""		,padding=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		tokenizer(sentences[0]		,return_tensors="""tf"""			)
           SCREAMING_SNAKE_CASE						=		model.generate(**_lowerCamelCase		,do_sample=_lowerCamelCase		,num_beams=2			)
           SCREAMING_SNAKE_CASE						=		(
               shape_list(inputs_non_padded["""input_ids"""]			)[-1]
               - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1]		,tf.intaa			)			).numpy()
           )
           SCREAMING_SNAKE_CASE						=		tokenizer(sentences[1]		,return_tensors="""tf"""			)
           SCREAMING_SNAKE_CASE						=		model.generate(
               **_lowerCamelCase		,do_sample=_lowerCamelCase		,num_beams=2		,max_length=model.config.max_length - num_paddings			)
           SCREAMING_SNAKE_CASE						=		tokenizer.decode(output_non_padded[0]		,skip_special_tokens=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		tokenizer.decode(output_padded[0]		,skip_special_tokens=_lowerCamelCase			)
           self.assertListEqual(_lowerCamelCase		,[non_padded_sentence, padded_sentence]			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self._get_beam_search_test_objects()

           SCREAMING_SNAKE_CASE						=		tokenizer(_lowerCamelCase		,return_tensors="""tf"""		,padding=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		tf.function(model.generate		,jit_compile=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		xla_generate(**_lowerCamelCase		,do_sample=_lowerCamelCase		,num_beams=2			)
           SCREAMING_SNAKE_CASE						=		tokenizer.batch_decode(_lowerCamelCase		,skip_special_tokens=_lowerCamelCase			)
           self.assertListEqual(_lowerCamelCase		,_lowerCamelCase			)








"""simple docstring"""

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowerCamelCase	:      Dict   =							16
_lowerCamelCase	:      Union[str, Any]   =							32

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if args.with_tracking:
		A_	:					Optional[int]						 =		Accelerator(
		    cpu=args.cpu	,   mixed_precision=args.mixed_precision	,   log_with='''all'''	,   project_dir=args.project_dir						)
	else:
		A_	:					str						 =		Accelerator(cpu=args.cpu	,   mixed_precision=args.mixed_precision						)

	if hasattr(args.checkpointing_steps	,   '''isdigit'''						):
		if args.checkpointing_steps == "epoch":
			A_	:					Optional[int]						 =		args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			A_	:					Dict						 =		int(args.checkpointing_steps						)
		else:
			raise ValueError(
			    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""						)
	else:
		A_	:					Dict						 =		None
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A_	:					List[Any]						 =		config['''lr''']
	A_	:					Optional[Any]						 =		int(config['''num_epochs''']						)
	A_	:					Optional[Any]						 =		int(config['''seed''']						)
	A_	:					List[str]						 =		int(config['''batch_size''']						)

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
		A_	:					Dict						 =		os.path.split(_UpperCAmelCase						)[-1].split('''.'''						)[0]
		accelerator.init_trackers(_UpperCAmelCase	,   _UpperCAmelCase						)

	A_	:					Optional[Any]						 =		AutoTokenizer.from_pretrained('''bert-base-cased'''						)
	A_	:					Any						 =		load_dataset('''glue'''	,   '''mrpc'''						)
	A_	:					Optional[int]						 =		evaluate.load('''glue'''	,   '''mrpc'''						)

	def tokenize_function(_UpperCAmelCase						):
		# max_length=None => use the model max length (it's actually the default)
		A_	:					Any						 =		tokenizer(examples['''sentence1''']	,   examples['''sentence2''']	,   truncation=_UpperCAmelCase	,   max_length=_UpperCAmelCase						)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		A_	:					List[Any]						 =		datasets.map(
		    _UpperCAmelCase	,   batched=_UpperCAmelCase	,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,   )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	A_	:					Any						 =		tokenized_datasets.rename_column('''label'''	,   '''labels'''						)

	# If the batch size is too big we use gradient accumulation
	A_	:					Optional[int]						 =		1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A_	:					Union[str, Any]						 =		batch_size // MAX_GPU_BATCH_SIZE
		A_	:					List[str]						 =		MAX_GPU_BATCH_SIZE

	def collate_fn(_UpperCAmelCase						):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A_	:					Union[str, Any]						 =		128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			A_	:					Tuple						 =		16
		elif accelerator.mixed_precision != "no":
			A_	:					Dict						 =		8
		else:
			A_	:					Tuple						 =		None

		return tokenizer.pad(
		    _UpperCAmelCase	,   padding='''longest'''	,   max_length=_UpperCAmelCase	,   pad_to_multiple_of=_UpperCAmelCase	,   return_tensors='''pt'''	,   )

	# Instantiate dataloaders.
	A_	:					List[Any]						 =		DataLoader(
	    tokenized_datasets['''train''']	,   shuffle=_UpperCAmelCase	,   collate_fn=_UpperCAmelCase	,   batch_size=_UpperCAmelCase						)
	A_	:					Optional[int]						 =		DataLoader(
	    tokenized_datasets['''validation''']	,   shuffle=_UpperCAmelCase	,   collate_fn=_UpperCAmelCase	,   batch_size=_UpperCAmelCase						)

	set_seed(_UpperCAmelCase						)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A_	:					Optional[int]						 =		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''	,   return_dict=_UpperCAmelCase						)

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	A_	:					str						 =		model.to(accelerator.device						)

	# Instantiate optimizer
	A_	:					str						 =		AdamW(params=model.parameters()	,   lr=_UpperCAmelCase						)

	# Instantiate scheduler
	A_	:					List[str]						 =		get_linear_schedule_with_warmup(
	    optimizer=_UpperCAmelCase	,   num_warmup_steps=100	,   num_training_steps=(len(_UpperCAmelCase						) * num_epochs) // gradient_accumulation_steps	,   )

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	A_ ,			A_ ,			A_ ,			A_ ,			A_	:					Optional[int]						 =		accelerator.prepare(
	    _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						)

	# We need to keep track of how many total steps we have iterated over
	A_	:					Tuple						 =		0
	# We also need to keep track of the stating epoch so files are named properly
	A_	:					Union[str, Any]						 =		0

	# Potentially load in the weights and states from a previous save
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""						)
			accelerator.load_state(args.resume_from_checkpoint						)
			A_	:					Union[str, Any]						 =		os.path.basename(args.resume_from_checkpoint						)
		else:
			# Get the most recent checkpoint
			A_	:					Union[str, Any]						 =		[f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
			dirs.sort(key=os.path.getctime						)
			A_	:					Optional[int]						 =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		A_	:					Any						 =		os.path.splitext(_UpperCAmelCase						)[0]

		if "epoch" in training_difference:
			A_	:					Any						 =		int(training_difference.replace('''epoch_'''	,   ''''''						)						) + 1
			A_	:					str						 =		None
		else:
			A_	:					str						 =		int(training_difference.replace('''step_'''	,   ''''''						)						)
			A_	:					List[str]						 =		resume_step // len(_UpperCAmelCase						)
			resume_step -= starting_epoch * len(_UpperCAmelCase						)

    # Now we train the model
	for epoch in range(_UpperCAmelCase	,   _UpperCAmelCase						):
		model.train()
		if args.with_tracking:
			A_	:					Optional[Any]						 =		0
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			A_	:					int						 =		accelerator.skip_first_batches(_UpperCAmelCase	,   _UpperCAmelCase						)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			A_	:					Dict						 =		train_dataloader
		for step, batch in enumerate(_UpperCAmelCase						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			A_	:					Any						 =		model(**_UpperCAmelCase						)
			A_	:					Any						 =		outputs.loss
			A_	:					str						 =		loss / gradient_accumulation_steps
			# We keep track of the loss at each epoch
			if args.with_tracking:
				total_loss += loss.detach().float()
			accelerator.backward(_UpperCAmelCase						)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

			if isinstance(_UpperCAmelCase	,   _UpperCAmelCase						):
				A_	:					List[Any]						 =		f"""step_{overall_step}"""
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						A_	:					Tuple						 =		os.path.join(args.output_dir	,   _UpperCAmelCase						)
					accelerator.save_state(_UpperCAmelCase						)

		model.eval()
		for step, batch in enumerate(_UpperCAmelCase						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			with torch.no_grad():
				A_	:					Dict						 =		model(**_UpperCAmelCase						)
			A_	:					List[str]						 =		outputs.logits.argmax(dim=-1						)
			A_ ,			A_	:					Optional[int]						 =		accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
			metric.add_batch(
			    predictions=_UpperCAmelCase	,   references=_UpperCAmelCase	,   )

		A_	:					Union[str, Any]						 =		metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""	,   _UpperCAmelCase						)
		if args.with_tracking:
			accelerator.log(
			    {
			        '''accuracy''': eval_metric['''accuracy'''],
			        '''f1''': eval_metric['''f1'''],
			        '''train_loss''': total_loss.item() / len(_UpperCAmelCase						),
			        '''epoch''': epoch,
			    }	,   step=_UpperCAmelCase	,   )

		if checkpointing_steps == "epoch":
			A_	:					str						 =		f"""epoch_{epoch}"""
			if args.output_dir is not None:
				A_	:					int						 =		os.path.join(args.output_dir	,   _UpperCAmelCase						)
			accelerator.save_state(_UpperCAmelCase						)

	if args.with_tracking:
		accelerator.end_training()

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		argparse.ArgumentParser(description='''Simple example of training script.'''						)
	parser.add_argument(
	    '''--mixed_precision'''	,   type=_UpperCAmelCase	,   default=_UpperCAmelCase	,   choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	,   help='''Whether to use mixed precision. Choose'''
	    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
	    '''and an Nvidia Ampere GPU.'''	,   )
	parser.add_argument('''--cpu'''	,   action='''store_true'''	,   help='''If passed, will train on the CPU.'''						)
	parser.add_argument(
	    '''--checkpointing_steps'''	,   type=_UpperCAmelCase	,   default=_UpperCAmelCase	,   help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''	,   )
	parser.add_argument(
	    '''--resume_from_checkpoint'''	,   type=_UpperCAmelCase	,   default=_UpperCAmelCase	,   help='''If the training should continue from a checkpoint folder.'''	,   )
	parser.add_argument(
	    '''--with_tracking'''	,   action='''store_true'''	,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,   )
	parser.add_argument(
	    '''--output_dir'''	,   type=_UpperCAmelCase	,   default='''.'''	,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,   )
	parser.add_argument(
	    '''--project_dir'''	,   type=_UpperCAmelCase	,   default='''logs'''	,   help='''Location on where to store experiment tracking logs` and relevent project information'''	,   )
	A_	:					Any						 =		parser.parse_args()
	A_	:					List[Any]						 =		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
	training_function(_UpperCAmelCase	,   _UpperCAmelCase						)


if __name__ == "__main__":
		main()





