

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							:	Tuple , a							:	List[Any]		)							->    Optional[int]:

				"""simple docstring"""

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

				"""simple docstring"""

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

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

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

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

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    None
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    None
				SCREAMING_SNAKE_CASE				: Any			       =    None
				if self.use_labels:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    ids_tensor([self.batch_size] , self.type_sequence_label_size		)
					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    ids_tensor([self.batch_size, self.seq_length] , self.num_labels		)
					SCREAMING_SNAKE_CASE				: str			       =    ids_tensor([self.batch_size] , self.num_choices		)

				SCREAMING_SNAKE_CASE				: Optional[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=a , )

				SCREAMING_SNAKE_CASE				: List[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							:	Tuple , a							:	str , a							:	List[str] , a							:	Dict , a							:	int , a							:	List[str] , *a							:	int		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    TFGPTJModel(config=a		)
				SCREAMING_SNAKE_CASE				: List[Any]			       =    {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				SCREAMING_SNAKE_CASE				: List[Any]			       =    model(a		)

				SCREAMING_SNAKE_CASE				: Dict			       =    [input_ids, None, input_mask]  # None is the input for 'past'
				SCREAMING_SNAKE_CASE				: List[str]			       =    model(a		)

				SCREAMING_SNAKE_CASE				: str			       =    model(a		)

				self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)		)
			def 							__UpperCamelCase	(     self							:	Tuple , a							:	str , a							:	Any , a							:	List[str] , a							:	str , a							:	str , *a							:	List[Any]		)							->    List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    TFGPTJModel(config=a		)

				# first forward pass
				SCREAMING_SNAKE_CASE				: List[str]			       =    model(a , token_type_ids=a , use_cache=a		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    model(a , token_type_ids=a		)
				SCREAMING_SNAKE_CASE				: Dict			       =    model(a , token_type_ids=a , use_cache=a		)

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

				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Any			       =    outputs.to_tuple()

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

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

				SCREAMING_SNAKE_CASE				: int			       =    model(a , token_type_ids=a		)["last_hidden_state"]
				SCREAMING_SNAKE_CASE				: Tuple			       =    model(a , token_type_ids=a , past_key_values=a		)[
				    "last_hidden_state"
				]

				# select random slice
				SCREAMING_SNAKE_CASE				: List[str]			       =    int(ids_tensor((1,) , shape_list(a		)[-1]		)		)
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    output_from_no_past[:, -1, random_slice_idx]
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    output_from_past[:, 0, random_slice_idx]

				# test that outputs are equal for slice
				tf.debugging.assert_near(a , a , rtol=1e-6		)
			def 							__UpperCamelCase	(     self							:	str , a							:	Tuple , a							:	int , a							:	Dict , a							:	Dict , a							:	int , *a							:	Optional[Any]		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[Any]			       =    TFGPTJModel(config=a		)

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

				# first forward pass
				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Any			       =    model(a , attention_mask=a		).to_tuple()

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

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

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

				# get two different outputs
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    model(a , attention_mask=a		)["last_hidden_state"]
				SCREAMING_SNAKE_CASE				: int			       =    model(a , past_key_values=a , attention_mask=a		)[
				    "last_hidden_state"
				]

				# select random slice
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    int(ids_tensor((1,) , shape_list(a		)[-1]		)		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    output_from_no_past[:, -1, random_slice_idx]
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    output_from_past[:, 0, random_slice_idx]

				# test that outputs are equal for slice
				tf.debugging.assert_near(a , a , rtol=1e-12		)
			def 							__UpperCamelCase	(     self							:	Tuple , a							:	Optional[int] , a							:	Tuple , a							:	Union[str, Any] , a							:	List[Any] , a							:	Any , *a							:	Union[str, Any]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    TFGPTJModel(config=a		)

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

				# first forward pass
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    model(a , attention_mask=a , token_type_ids=a , use_cache=a		)

				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: List[str]			       =    outputs.to_tuple()

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

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

				SCREAMING_SNAKE_CASE				: List[Any]			       =    model(
				    a , token_type_ids=a , attention_mask=a		)["last_hidden_state"]
				SCREAMING_SNAKE_CASE				: List[str]			       =    model(
				    a , token_type_ids=a , attention_mask=a , past_key_values=a , )["last_hidden_state"]
				self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]		)

				# select random slice
				SCREAMING_SNAKE_CASE				: int			       =    int(ids_tensor((1,) , shape_list(a		)[-1]		)		)
				SCREAMING_SNAKE_CASE				: Tuple			       =    output_from_no_past[:, -3:, random_slice_idx]
				SCREAMING_SNAKE_CASE				: List[str]			       =    output_from_past[:, :, random_slice_idx]

				# test that outputs are equal for slice
				tf.debugging.assert_near(a , a , rtol=1e-3		)
			def 							__UpperCamelCase	(     self							:	List[str] , a							:	List[str] , a							:	Tuple , a							:	Optional[int] , a							:	List[str] , a							:	str , *a							:	int		)							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    TFGPTJForCausalLM(config=a		)
				SCREAMING_SNAKE_CASE				: List[Any]			       =    {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    model(a		)
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size)		)






			def 							__UpperCamelCase	(     self							:	List[Any]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    self.prepare_config_and_inputs()

				(
				    (
				    SCREAMING_SNAKE_CASE
				)       ,(
				    SCREAMING_SNAKE_CASE
				)       ,(
				    SCREAMING_SNAKE_CASE
				)       ,(
				    SCREAMING_SNAKE_CASE
				)       ,(
				    SCREAMING_SNAKE_CASE
				)       ,(
				    SCREAMING_SNAKE_CASE
				)       ,(
				    SCREAMING_SNAKE_CASE
				)       ,(
				    SCREAMING_SNAKE_CASE
				)       ,(
				    SCREAMING_SNAKE_CASE
				)       ,
				)				: List[str]			       =    config_and_inputs

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    {
				    "input_ids": input_ids,
				    "token_type_ids": token_type_ids,
				    "attention_mask": input_mask,
				}
				return config, inputs_dict






@require_tf
class _UpperCamelCase							(							__A							,		__A							,		__A							,		unittest.TestCase				):
			'''simple docstring'''







			lowerCamelCase__          =(
			    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
			    if is_tf_available()
			    else ()
			)

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    TFGPTJModelTester(self		)
				SCREAMING_SNAKE_CASE				: int			       =    ConfigTester(self , config_class=a , n_embd=37		)
			def 							__UpperCamelCase	(     self							:	List[str]		)							->    str:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_gptj_model(*a		)
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[Any]			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_gptj_model_past(*a		)
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_gptj_model_attention_mask_past(*a		)
			def 							__UpperCamelCase	(     self							:	Dict		)							->    Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_gptj_model_past_large_inputs(*a		)
			def 							__UpperCamelCase	(     self							:	Dict		)							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_gptj_lm_head_model(*a		)
			@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							:	Union[str, Any]		)							->    int:

				"""simple docstring"""

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






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







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" , from_pt=a		)
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    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				: str			       =    [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 1_5185, 11, 290, 340, 318, 257, 1545]
				# fmt: on
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    model.generate(a , do_sample=a		)
				self.assertListEqual(output_ids[0].numpy().tolist() , a		)
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    str:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE				: Union[str, 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"		):
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    model.generate(**a , do_sample=a , seed=[42, 0]		)
				SCREAMING_SNAKE_CASE				: List[Any]			       =    tokenizer.decode(output_ids[0] , skip_special_tokens=a		)

				SCREAMING_SNAKE_CASE				: Tuple			       =    "Today is a nice day and I’m going to go for a walk. I’"
				self.assertEqual(a , a		)
			def 							__UpperCamelCase	(     self							:	Tuple		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" , revision="float16" , from_pt=a		)
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B" , revision="float16"		)

				SCREAMING_SNAKE_CASE				: Any			       =    "left"

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

				# use different length sentences to test batching
				SCREAMING_SNAKE_CASE				: str			       =    [
				    "Hello, my dog is a little",
				    "Today, I",
				]
				SCREAMING_SNAKE_CASE				: Union[str, 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 							__UpperCamelCase	(     self							:	Optional[int]		)							->    Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self._get_beam_search_test_objects()

				SCREAMING_SNAKE_CASE				: List[Any]			       =    tokenizer(a , return_tensors="tf" , padding=a		)
				SCREAMING_SNAKE_CASE				: str			       =    model.generate(**a , do_sample=a , num_beams=2		)
				SCREAMING_SNAKE_CASE				: Dict			       =    tokenizer.batch_decode(a , skip_special_tokens=a		)
				self.assertListEqual(a , a		)
			def 							__UpperCamelCase	(     self							:	List[str]		)							->    Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Tuple			       =    self._get_beam_search_test_objects()

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






			def 							__UpperCamelCase	(     self							:	List[str]		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self._get_beam_search_test_objects()

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


import math
import os

import cva
import numpy as np

a_         =      255.0

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(_a))

def 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: List[Any]			       =    os.path.dirname(os.path.realpath(_a))
	# Loading images (original image and compressed image)
	SCREAMING_SNAKE_CASE				: Any			       =    cva.imread(os.path.join(_a							,     "image_data/original_image.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(os.path.join(_a							,     "image_data/compressed_image.png")							,     1)

	SCREAMING_SNAKE_CASE				: List[Any]			       =    cva.imread(os.path.join(_a							,     "image_data/PSNR-example-base.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(
	    os.path.join(_a							,     "image_data/PSNR-example-comp-10.jpg")							,     1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(f"PSNR value is {peak_signal_to_noise_ratio(_a							,     _a)} dB")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(f"PSNR value is {peak_signal_to_noise_ratio(_a							,     _a)} dB")


if __name__ == "__main__":
			main()