


from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel







@require_tf
class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						BlenderbotConfig
							_UpperCAmelCase      =						{}
							_UpperCAmelCase      =						"""gelu"""






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=7			,						lowerCAmelCase__=True			,						lowerCAmelCase__=False			,						lowerCAmelCase__=9_9			,						lowerCAmelCase__=3_2			,						lowerCAmelCase__=2			,						lowerCAmelCase__=4			,						lowerCAmelCase__=3_7			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=2_0			,						lowerCAmelCase__=2			,						lowerCAmelCase__=1			,						lowerCAmelCase__=0			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					parent
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					seq_length
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					is_training
										SCREAMING_SNAKE_CASE_		:     Dict					=					use_labels
										SCREAMING_SNAKE_CASE_		:     Any					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     int					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Any					=					intermediate_size

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     List[str]					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Dict					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					eos_token_id
										SCREAMING_SNAKE_CASE_		:     str					=					pad_token_id
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					bos_token_id






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					ids_tensor([self.batch_size, self.seq_length - 1]			,						self.vocab_size       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )			,						1       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tf.concat([input_ids, eos_tensor]			,						axis=1       )

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

										SCREAMING_SNAKE_CASE_		:     Any					=					self.config_cls(
										    vocab_size=self.vocab_size			,						d_model=self.hidden_size			,						encoder_layers=self.num_hidden_layers			,						decoder_layers=self.num_hidden_layers			,						encoder_attention_heads=self.num_attention_heads			,						decoder_attention_heads=self.num_attention_heads			,						encoder_ffn_dim=self.intermediate_size			,						decoder_ffn_dim=self.intermediate_size			,						dropout=self.hidden_dropout_prob			,						attention_dropout=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						eos_token_ids=[2]			,						bos_token_id=self.bos_token_id			,						pad_token_id=self.pad_token_id			,						decoder_start_token_id=self.pad_token_id			,						**self.config_updates			,						)
										SCREAMING_SNAKE_CASE_		:     Dict					=					prepare_blenderbot_inputs_dict(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )
										return config, inputs_dict






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					TFBlenderbotModel(config=lowerCAmelCase__       ).get_decoder()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					inputs_dict['input_ids']

										SCREAMING_SNAKE_CASE_		:     str					=					input_ids[:1, :]
										SCREAMING_SNAKE_CASE_		:     List[str]					=					inputs_dict['attention_mask'][:1, :]
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					inputs_dict['head_mask']
										SCREAMING_SNAKE_CASE_		:     Dict					=					1

										# first forward pass
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					model(lowerCAmelCase__			,						attention_mask=lowerCAmelCase__			,						head_mask=lowerCAmelCase__			,						use_cache=lowerCAmelCase__       )

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

										# create hypothetical next token and extent to next_input_ids
										SCREAMING_SNAKE_CASE_		:     Dict					=					ids_tensor((self.batch_size, 3)			,						config.vocab_size       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					tf.cast(ids_tensor((self.batch_size, 3)			,						2       )			,						tf.inta       )

										# append to next input_ids and
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tf.concat([input_ids, next_tokens]			,						axis=-1       )
										SCREAMING_SNAKE_CASE_		:     Any					=					tf.concat([attention_mask, next_attn_mask]			,						axis=-1       )

										SCREAMING_SNAKE_CASE_		:     str					=					model(lowerCAmelCase__			,						attention_mask=lowerCAmelCase__       )[0]
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					model(lowerCAmelCase__			,						attention_mask=lowerCAmelCase__			,						past_key_values=lowerCAmelCase__       )[0]

										self.parent.assertEqual(next_tokens.shape[1]			,						output_from_past.shape[1]       )

										# select random slice
										SCREAMING_SNAKE_CASE_		:     Tuple					=					int(ids_tensor((1,)			,						output_from_past.shape[-1]       )       )
										SCREAMING_SNAKE_CASE_		:     str					=					output_from_no_past[:, -3:, random_slice_idx]
										SCREAMING_SNAKE_CASE_		:     Tuple					=					output_from_past[:, :, random_slice_idx]

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







def 						a__			(				A__,      A__,      A__,      A__=None,      A__=None,      A__=None,      A__=None,      A__=None,      ):
			if attention_mask is None:
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tf.cast(tf.math.not_equal(A__,      config.pad_token_id				),      tf.inta				)
			if decoder_attention_mask is None:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape,      dtype=tf.inta				),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],      config.pad_token_id				),      tf.inta				),
						    ],      axis=-1,      )
			if head_mask is None:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
			if decoder_head_mask is None:
						SCREAMING_SNAKE_CASE_		:     Dict					=					tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
			if cross_attn_head_mask is None:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}







@require_tf
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
							_UpperCAmelCase      =						(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
							_UpperCAmelCase      =						(
							    {
							        """conversational""": TFBlenderbotForConditionalGeneration,
							        """feature-extraction""": TFBlenderbotModel,
							        """summarization""": TFBlenderbotForConditionalGeneration,
							        """text2text-generation""": TFBlenderbotForConditionalGeneration,
							        """translation""": TFBlenderbotForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							_UpperCAmelCase      =						True
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					TFBlenderbotModelTester(self       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					ConfigTester(self			,						config_class=lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										self.config_tester.run_common_tests()






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__       )







@require_tokenizers
@require_tf
class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""My friends are cool but they eat too many carbs."""]
							_UpperCAmelCase      =						"""facebook/blenderbot-400M-distill"""






							@cached_property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return BlenderbotTokenizer.from_pretrained(self.model_name       )






							@cached_property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
										return model






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.tokenizer(self.src_text			,						return_tensors='tf'       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.model.generate(
										    model_inputs.input_ids			,						)
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.tokenizer.batch_decode(generated_ids.numpy()			,						skip_special_tokens=lowerCAmelCase__       )[0]
										assert (
										    generated_words
										    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
										)













from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Union[List[PIL.Image.Image], np.ndarray]
       __lowerCAmelCase    :		Optional[List[bool]]


if is_transformers_available() and is_torch_available():
    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


