






'''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 					a__    :
     '''simple docstring'''







     def __init__(self    :	Union[str, Any]				,				lowercase__    :	List[str]					)  ->       Any:
           __A		   =      parent
           __A		   =      13
           __A		   =      7
           __A		   =      True
           __A		   =      True
           __A		   =      True
           __A		   =      True
           __A		   =      True
           __A		   =      99
           __A		   =      32
           __A		   =      4
           __A		   =      2
           __A		   =      4
           __A		   =      37
           __A		   =      "gelu"
           __A		   =      0.1
           __A		   =      0.1
           __A		   =      512
           __A		   =      16
           __A		   =      2
           __A		   =      0.02
           __A		   =      3
           __A		   =      4
           __A		   =      None
           __A		   =      self.vocab_size - 1
           __A		   =      self.vocab_size - 1
           __A		   =      self.vocab_size - 1






     def     _UpperCamelCase   (self    :	List[str]					)  ->       List[Any]:
           __A		   =      ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size					)

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

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

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

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

           __A		   =      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=lowercase__				,				)

           __A		   =      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    :	List[Any]				,				lowercase__    :	int				,				lowercase__    :	int				,				lowercase__    :	Optional[int]				,				lowercase__    :	Any				,				lowercase__    :	str				,				*lowercase__    :	Tuple					)  ->       Optional[int]:
           __A		   =      TFGPTJModel(config=lowercase__					)
           __A		   =      {
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }
           __A		   =      model(lowercase__					)

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

           __A		   =      model(lowercase__					)

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






     def     _UpperCamelCase   (self    :	int				,				lowercase__    :	Optional[int]				,				lowercase__    :	int				,				lowercase__    :	Tuple				,				lowercase__    :	str				,				lowercase__    :	Dict				,				*lowercase__    :	int					)  ->       List[Any]:
           __A		   =      TFGPTJModel(config=lowercase__					)

           # first forward pass
           __A		   =      model(lowercase__				,				token_type_ids=lowercase__				,				use_cache=lowercase__					)
           __A		   =      model(lowercase__				,				token_type_ids=lowercase__					)
           __A		   =      model(lowercase__				,				token_type_ids=lowercase__				,				use_cache=lowercase__					)

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

           __A   ,				__A		   =      outputs.to_tuple()

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

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

           __A		   =      model(lowercase__				,				token_type_ids=lowercase__					)["last_hidden_state"]
           __A		   =      model(lowercase__				,				token_type_ids=lowercase__				,				past_key_values=lowercase__					)[
               "last_hidden_state"
           ]

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

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






     def     _UpperCamelCase   (self    :	List[str]				,				lowercase__    :	str				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	int				,				lowercase__    :	Optional[Any]				,				lowercase__    :	Tuple				,				*lowercase__    :	Optional[int]					)  ->       List[Any]:
           __A		   =      TFGPTJModel(config=lowercase__					)

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

           # first forward pass
           __A   ,				__A		   =      model(lowercase__				,				attention_mask=lowercase__					).to_tuple()

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

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

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

           # get two different outputs
           __A		   =      model(lowercase__				,				attention_mask=lowercase__					)["last_hidden_state"]
           __A		   =      model(lowercase__				,				past_key_values=lowercase__				,				attention_mask=lowercase__					)[
               "last_hidden_state"
           ]

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

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






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Dict				,				lowercase__    :	Optional[Any]				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Tuple				,				lowercase__    :	Union[str, Any]				,				*lowercase__    :	Optional[Any]					)  ->       List[Any]:
           __A		   =      TFGPTJModel(config=lowercase__					)

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

           # first forward pass
           __A		   =      model(lowercase__				,				attention_mask=lowercase__				,				token_type_ids=lowercase__				,				use_cache=lowercase__					)

           __A   ,				__A		   =      outputs.to_tuple()

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

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

           __A		   =      model(
               lowercase__				,				token_type_ids=lowercase__				,				attention_mask=lowercase__					)["last_hidden_state"]
           __A		   =      model(
               lowercase__				,				token_type_ids=lowercase__				,				attention_mask=lowercase__				,				past_key_values=lowercase__				,				)["last_hidden_state"]
           self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]					)

           # select random slice
           __A		   =      int(ids_tensor((1,)				,				shape_list(lowercase__					)[-1]					)					)
           __A		   =      output_from_no_past[:, -3:, random_slice_idx]
           __A		   =      output_from_past[:, :, random_slice_idx]

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






     def     _UpperCamelCase   (self    :	Optional[Any]				,				lowercase__    :	List[Any]				,				lowercase__    :	int				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Any				,				lowercase__    :	Optional[Any]				,				*lowercase__    :	List[Any]					)  ->       Optional[Any]:
           __A		   =      TFGPTJForCausalLM(config=lowercase__					)
           __A		   =      {
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }
           __A		   =      model(lowercase__					)
           self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)					)






     def     _UpperCamelCase   (self    :	Tuple					)  ->       int:
           __A		   =      self.prepare_config_and_inputs()

           (
               (
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				
           )		   =      config_and_inputs

           __A		   =      {
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
               "attention_mask": input_mask,
           }
           return config, inputs_dict







@require_tf
class 					a__    (			lowerCamelCase ,						lowerCamelCase ,						lowerCamelCase ,						unittest.TestCase						):
     '''simple docstring'''







     _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    :	Union[str, Any]				,				lowercase__    :	Dict				,				lowercase__    :	List[str]				,				lowercase__    :	List[Any]				,				lowercase__    :	Optional[int]				,				lowercase__    :	Any					)  ->       Optional[Any]:
           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    :	Dict					)  ->       Optional[Any]:
           __A		   =      TFGPTJModelTester(self					)
           __A		   =      ConfigTester(self				,				config_class=lowercase__				,				n_embd=37					)






     def     _UpperCamelCase   (self    :	Any					)  ->       str:
           self.config_tester.run_common_tests()






     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       Any:
           __A		   =      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_gptj_model(*lowercase__					)






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       List[Any]:
           __A		   =      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_gptj_model_past(*lowercase__					)






     def     _UpperCamelCase   (self    :	List[str]					)  ->       List[str]:
           __A		   =      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_gptj_model_attention_mask_past(*lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       List[Any]:
           __A		   =      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_gptj_model_past_large_inputs(*lowercase__					)






     def     _UpperCamelCase   (self    :	int					)  ->       str:
           __A		   =      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_gptj_lm_head_model(*lowercase__					)






     @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					)  ->       str:
           __A		   =      TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B"				,				from_pt=lowercase__					)
           self.assertIsNotNone(lowercase__					)






     @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."					)
     def     _UpperCamelCase   (self    :	int					)  ->       int:
           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 					a__    (			unittest.TestCase						):
     '''simple docstring'''







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






     def     _UpperCamelCase   (self    :	int					)  ->       int:
           __A		   =      AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"				,				revision="float16"					)
           __A		   =      TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"				,				revision="float16"				,				from_pt=lowercase__					)

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

           __A		   =      "Today is a nice day and I’m going to go for a walk. I’"
           self.assertEqual(lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Tuple:
           __A		   =      TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"				,				revision="float16"				,				from_pt=lowercase__					)
           __A		   =      AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"				,				revision="float16"					)

           __A		   =      "left"

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

           # use different length sentences to test batching
           __A		   =      [
               "Hello, my dog is a little",
               "Today, I",
           ]
           __A		   =      [
               "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    :	Optional[Any]					)  ->       List[str]:
           # Confirms that we get the expected results with left-padded beam search
           __A   ,				__A   ,				__A   ,				__A		   =      self._get_beam_search_test_objects()

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






     def     _UpperCamelCase   (self    :	Dict					)  ->       Any:
           # Confirms that left-padding is working properly
           __A   ,				__A   ,				__A   ,				__A		   =      self._get_beam_search_test_objects()

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






     def     _UpperCamelCase   (self    :	List[str]					)  ->       int:
           # Confirms that XLA is working properly
           __A   ,				__A   ,				__A   ,				__A		   =      self._get_beam_search_test_objects()

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










'''simple docstring'''







def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
      if n < 0:
            return 0
      # table[i] represents the number of ways to get to amount i
      __A		   =      [0] * (n + 1)

      # There is exactly 1 way to get to zero(You pick no coins).
      __A		   =      1

      # Pick all coins one by one and update table[] values
      # after the index greater than or equal to the value of the
      # picked coin
      for coin_val in s:
            for j in range(snake_case__							,			n + 1							):
                  table[j] += table[j - coin_val]

      return table[n]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


