

"""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 ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							parent
        lowerCAmelCase__      :							str    =							13
        lowerCAmelCase__      :							Optional[int]    =							7
        lowerCAmelCase__      :							Optional[Any]    =							True
        lowerCAmelCase__      :							Tuple    =							True
        lowerCAmelCase__      :							List[str]    =							True
        lowerCAmelCase__      :							Union[str, Any]    =							True
        lowerCAmelCase__      :							Any    =							True
        lowerCAmelCase__      :							List[str]    =							99
        lowerCAmelCase__      :							Union[str, Any]    =							32
        lowerCAmelCase__      :							Dict    =							4
        lowerCAmelCase__      :							Dict    =							2
        lowerCAmelCase__      :							Union[str, Any]    =							4
        lowerCAmelCase__      :							Optional[Any]    =							37
        lowerCAmelCase__      :							List[str]    =							"gelu"
        lowerCAmelCase__      :							Dict    =							0.1
        lowerCAmelCase__      :							str    =							0.1
        lowerCAmelCase__      :							Optional[Any]    =							512
        lowerCAmelCase__      :							Optional[Any]    =							16
        lowerCAmelCase__      :							Tuple    =							2
        lowerCAmelCase__      :							List[Any]    =							0.02
        lowerCAmelCase__      :							Optional[Any]    =							3
        lowerCAmelCase__      :							Optional[Any]    =							4
        lowerCAmelCase__      :							Dict    =							None
        lowerCAmelCase__      :							Optional[int]    =							self.vocab_size - 1
        lowerCAmelCase__      :							Optional[int]    =							self.vocab_size - 1
        lowerCAmelCase__      :							Optional[int]    =							self.vocab_size - 1






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

        lowerCAmelCase__      :							Optional[Any]    =							None
        if self.use_input_mask:
         lowerCAmelCase__      :							Optional[Any]    =							random_attention_mask([self.batch_size, self.seq_length]      )

        lowerCAmelCase__      :							List[Any]    =							None
        if self.use_token_type_ids:
         lowerCAmelCase__      :							List[str]    =							ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size      )

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

        lowerCAmelCase__      :							Optional[int]    =							None
        lowerCAmelCase__      :							Dict    =							None
        lowerCAmelCase__      :							str    =							None
        if self.use_labels:
         lowerCAmelCase__      :							int    =							ids_tensor([self.batch_size] ,				self.type_sequence_label_size      )
         lowerCAmelCase__      :							Optional[int]    =							ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels      )
         lowerCAmelCase__      :							List[str]    =							ids_tensor([self.batch_size] ,				self.num_choices      )

        lowerCAmelCase__      :							Union[str, Any]    =							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=snake_case ,				)

        lowerCAmelCase__      :							Union[str, Any]    =							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 ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				*snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							TFGPTJModel(config=snake_case      )
        lowerCAmelCase__      :							Optional[Any]    =							{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        lowerCAmelCase__      :							Optional[Any]    =							model(snake_case      )

        lowerCAmelCase__      :							int    =							[input_ids, None, input_mask]  # None is the input for 'past'
        lowerCAmelCase__      :							Optional[int]    =							model(snake_case      )

        lowerCAmelCase__      :							List[Any]    =							model(snake_case      )

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				*snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							TFGPTJModel(config=snake_case      )

        # first forward pass
        lowerCAmelCase__      :							Tuple    =							model(snake_case ,				token_type_ids=snake_case ,				use_cache=snake_case      )
        lowerCAmelCase__      :							Dict    =							model(snake_case ,				token_type_ids=snake_case      )
        lowerCAmelCase__      :							Dict    =							model(snake_case ,				token_type_ids=snake_case ,				use_cache=snake_case      )

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

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Tuple    =							outputs.to_tuple()

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

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

        lowerCAmelCase__      :							Any    =							model(snake_case ,				token_type_ids=snake_case      )["last_hidden_state"]
        lowerCAmelCase__      :							List[str]    =							model(snake_case ,				token_type_ids=snake_case ,				past_key_values=snake_case      )[
            "last_hidden_state"
        ]

        # select random slice
        lowerCAmelCase__      :							str    =							int(ids_tensor((1,) ,				shape_list(snake_case      )[-1]      )      )
        lowerCAmelCase__      :							Tuple    =							output_from_no_past[:, -1, random_slice_idx]
        lowerCAmelCase__      :							Union[str, Any]    =							output_from_past[:, 0, random_slice_idx]

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				*snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							TFGPTJModel(config=snake_case      )

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

        # first forward pass
        lowerCAmelCase__ ,	lowerCAmelCase__      :							str    =							model(snake_case ,				attention_mask=snake_case      ).to_tuple()

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

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

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

        # get two different outputs
        lowerCAmelCase__      :							Any    =							model(snake_case ,				attention_mask=snake_case      )["last_hidden_state"]
        lowerCAmelCase__      :							Union[str, Any]    =							model(snake_case ,				past_key_values=snake_case ,				attention_mask=snake_case      )[
            "last_hidden_state"
        ]

        # select random slice
        lowerCAmelCase__      :							int    =							int(ids_tensor((1,) ,				shape_list(snake_case      )[-1]      )      )
        lowerCAmelCase__      :							Tuple    =							output_from_no_past[:, -1, random_slice_idx]
        lowerCAmelCase__      :							str    =							output_from_past[:, 0, random_slice_idx]

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				*snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							TFGPTJModel(config=snake_case      )

        lowerCAmelCase__      :							Optional[Any]    =							input_ids[:1, :]
        lowerCAmelCase__      :							Dict    =							input_mask[:1, :]
        lowerCAmelCase__      :							Optional[Any]    =							token_type_ids[:1, :]
        lowerCAmelCase__      :							int    =							1

        # first forward pass
        lowerCAmelCase__      :							Union[str, Any]    =							model(snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				use_cache=snake_case      )

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							outputs.to_tuple()

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

        # append to next input_ids and token_type_ids
        lowerCAmelCase__      :							Union[str, Any]    =							tf.concat([input_ids, next_tokens] ,				axis=-1      )
        lowerCAmelCase__      :							Union[str, Any]    =							tf.concat([input_mask, next_attn_mask] ,				axis=-1      )
        lowerCAmelCase__      :							Optional[int]    =							tf.concat([token_type_ids, next_token_types] ,				axis=-1      )

        lowerCAmelCase__      :							Union[str, Any]    =							model(
            snake_case ,				token_type_ids=snake_case ,				attention_mask=snake_case      )["last_hidden_state"]
        lowerCAmelCase__      :							str    =							model(
            snake_case ,				token_type_ids=snake_case ,				attention_mask=snake_case ,				past_key_values=snake_case ,				)["last_hidden_state"]
        self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]      )

        # select random slice
        lowerCAmelCase__      :							int    =							int(ids_tensor((1,) ,				shape_list(snake_case      )[-1]      )      )
        lowerCAmelCase__      :							Optional[int]    =							output_from_no_past[:, -3:, random_slice_idx]
        lowerCAmelCase__      :							Dict    =							output_from_past[:, :, random_slice_idx]

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				*snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							TFGPTJForCausalLM(config=snake_case      )
        lowerCAmelCase__      :							Any    =							{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        lowerCAmelCase__      :							List[Any]    =							model(snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							self.prepare_config_and_inputs()

        (
            (
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	
        )      :							Optional[int]    =							config_and_inputs

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






@require_tf
class  __a				(	__magic_name__					,  __magic_name__					,  __magic_name__					,  unittest.TestCase					):





       """simple docstring"""


       __UpperCamelCase				:			List[Any]          =	(
           (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
           if is_tf_available()
           else ()
       )

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """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      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							TFGPTJModelTester(self      )
        lowerCAmelCase__      :							Any    =							ConfigTester(self ,				config_class=snake_case ,				n_embd=37      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.config_tester.run_common_tests()






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_gptj_model(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_gptj_model_past(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_gptj_model_attention_mask_past(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_gptj_model_past_large_inputs(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_gptj_lm_head_model(*snake_case      )






       @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      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B" ,				from_pt=snake_case      )
        self.assertIsNotNone(snake_case      )






       @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """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  __a				(	unittest.TestCase					):





       """simple docstring"""


       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" ,				from_pt=snake_case      )
        lowerCAmelCase__      :							List[str]    =							tf.convert_to_tensor([[464, 3_290]] ,				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
        lowerCAmelCase__      :							List[Any]    =							[464, 3_290, 318, 257, 582, 338, 1_266, 1_545, 13, 632, 318, 257, 9_112, 15_185, 11, 290, 340, 318, 257, 1_545]
        # fmt: on
        lowerCAmelCase__      :							Optional[int]    =							model.generate(snake_case ,				do_sample=snake_case      )
        self.assertListEqual(output_ids[0].numpy().tolist() ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B" ,				revision="float16"      )
        lowerCAmelCase__      :							Dict    =							TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" ,				revision="float16" ,				from_pt=snake_case      )

        lowerCAmelCase__      :							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"      ):
         lowerCAmelCase__      :							Optional[Any]    =							model.generate(**snake_case ,				do_sample=snake_case ,				seed=[42, 0]      )
        lowerCAmelCase__      :							str    =							tokenizer.decode(output_ids[0] ,				skip_special_tokens=snake_case      )

        lowerCAmelCase__      :							List[str]    =							"Today is a nice day and I’m going to go for a walk. I’"
        self.assertEqual(snake_case ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" ,				revision="float16" ,				from_pt=snake_case      )
        lowerCAmelCase__      :							List[Any]    =							AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B" ,				revision="float16"      )

        lowerCAmelCase__      :							Dict    =							"left"

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

        # use different length sentences to test batching
        lowerCAmelCase__      :							List[str]    =							[
            "Hello, my dog is a little",
            "Today, I",
        ]
        lowerCAmelCase__      :							Optional[Any]    =							[
            "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      ):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Optional[Any]    =							self._get_beam_search_test_objects()

        lowerCAmelCase__      :							List[str]    =							tokenizer(snake_case ,				return_tensors="tf" ,				padding=snake_case      )
        lowerCAmelCase__      :							Tuple    =							model.generate(**snake_case ,				do_sample=snake_case ,				num_beams=2      )
        lowerCAmelCase__      :							Optional[Any]    =							tokenizer.batch_decode(snake_case ,				skip_special_tokens=snake_case      )
        self.assertListEqual(snake_case ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							self._get_beam_search_test_objects()

        lowerCAmelCase__      :							Tuple    =							tokenizer(snake_case ,				return_tensors="tf" ,				padding=snake_case      )
        lowerCAmelCase__      :							str    =							tf.function(model.generate ,				jit_compile=snake_case      )
        lowerCAmelCase__      :							Dict    =							xla_generate(**snake_case ,				do_sample=snake_case ,				num_beams=2      )
        lowerCAmelCase__      :							str    =							tokenizer.batch_decode(snake_case ,				skip_special_tokens=snake_case      )
        self.assertListEqual(snake_case ,				snake_case      )









"""simple docstring"""

from itertools import product



def 			SCREAMING_SNAKE_CASE       (   lowercase__ = 1_5      ) ->	int:
 lowerCAmelCase__      :							float    =							0.0
 lowerCAmelCase__      :							float
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							tuple[int, ...]

 for series in product(range(2      )				,				repeat=lowercase__      ):
  lowerCAmelCase__      :							Optional[int]    =							series.count(1      )
  lowerCAmelCase__      :							Dict    =							num_turns - num_blue
  if num_red >= num_blue:
   continue
  lowerCAmelCase__      :							Dict    =							1.0
  for ind, col in enumerate(lowercase__				,				2      ):
   if col == 0:
    prob *= (ind - 1) / ind
   else:
    prob *= 1 / ind

  total_prob += prob

 return int(1 / total_prob      )


if __name__ == "__main__":
    print(f"{solution() = }")






