
from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class       a		:
							lowercase_		:						Dict   							=  BlenderbotSmallConfig
							lowercase_		:						Union[str, Any]   							=  {}
							lowercase_		:						int   							=  'gelu'







							def __init__(						self  :  Union[str, Any]      ,				snake_case__  :  Any      ,				snake_case__  :  Union[str, Any]=13      ,				snake_case__  :  List[str]=7      ,				snake_case__  :  Any=True      ,				snake_case__  :  List[Any]=False      ,				snake_case__  :  Union[str, Any]=99      ,				snake_case__  :  Union[str, Any]=32      ,				snake_case__  :  Any=2      ,				snake_case__  :  List[str]=4      ,				snake_case__  :  List[str]=37      ,				snake_case__  :  str=0.1      ,				snake_case__  :  Tuple=0.1      ,				snake_case__  :  Optional[Any]=20      ,				snake_case__  :  Dict=2      ,				snake_case__  :  Dict=1      ,				snake_case__  :  Any=0      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							parent
								__lowerCAmelCase         =							batch_size
								__lowerCAmelCase         =							seq_length
								__lowerCAmelCase         =							is_training
								__lowerCAmelCase         =							use_labels
								__lowerCAmelCase         =							vocab_size
								__lowerCAmelCase         =							hidden_size
								__lowerCAmelCase         =							num_hidden_layers
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							intermediate_size

								__lowerCAmelCase         =							hidden_dropout_prob
								__lowerCAmelCase         =							attention_probs_dropout_prob
								__lowerCAmelCase         =							max_position_embeddings
								__lowerCAmelCase         =							eos_token_id
								__lowerCAmelCase         =							pad_token_id
								__lowerCAmelCase         =							bos_token_id







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							ids_tensor([self.batch_size, self.seq_length - 1]      ,				self.vocab_size )
								__lowerCAmelCase         =							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )      ,				1 )
								__lowerCAmelCase         =							tf.concat([input_ids, eos_tensor]      ,				axis=1 )

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

								__lowerCAmelCase         =							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      ,				)
								__lowerCAmelCase         =							prepare_blenderbot_small_inputs_dict(snake_case__      ,				snake_case__      ,				snake_case__ )
								return config, inputs_dict







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Any      ,				snake_case__  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							TFBlenderbotSmallModel(config=snake_case__ ).get_decoder()
								__lowerCAmelCase         =							inputs_dict["input_ids"]

								__lowerCAmelCase         =							input_ids[:1, :]
								__lowerCAmelCase         =							inputs_dict["attention_mask"][:1, :]
								__lowerCAmelCase         =							inputs_dict["head_mask"]
								__lowerCAmelCase         =							1

								# first forward pass
								__lowerCAmelCase         =							model(snake_case__      ,				attention_mask=snake_case__      ,				head_mask=snake_case__      ,				use_cache=snake_case__ )

								__lowerCAmelCase   ,						__lowerCAmelCase         =							outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								__lowerCAmelCase         =							ids_tensor((self.batch_size, 3)      ,				config.vocab_size )
								__lowerCAmelCase         =							tf.cast(ids_tensor((self.batch_size, 3)      ,				2 )      ,				tf.inta )

								# append to next input_ids and
								__lowerCAmelCase         =							tf.concat([input_ids, next_tokens]      ,				axis=-1 )
								__lowerCAmelCase         =							tf.concat([attention_mask, next_attn_mask]      ,				axis=-1 )

								__lowerCAmelCase         =							model(snake_case__      ,				attention_mask=snake_case__ )[0]
								__lowerCAmelCase         =							model(snake_case__      ,				attention_mask=snake_case__      ,				past_key_values=snake_case__ )[0]

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

								# select random slice
								__lowerCAmelCase         =							int(ids_tensor((1,)      ,				output_from_past.shape[-1] ) )
								__lowerCAmelCase         =							output_from_no_past[:, -3:, random_slice_idx]
								__lowerCAmelCase         =							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 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     Tuple			,							UpperCamelCase:     Tuple			,							UpperCamelCase:     Optional[int]=None			,							UpperCamelCase:     Tuple=None			,							UpperCamelCase:     Union[str, Any]=None			,							UpperCamelCase:     Union[str, Any]=None			,							UpperCamelCase:     Any=None			,							):






	"""simple docstring"""


	if attention_mask is None:
		__lowerCAmelCase         =							tf.cast(tf.math.not_equal(UpperCamelCase			,							config.pad_token_id  )			,							tf.inta  )
	if decoder_attention_mask is None:
		__lowerCAmelCase         =							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:
		__lowerCAmelCase         =							tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
	if decoder_head_mask is None:
		__lowerCAmelCase         =							tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
	if cross_attn_head_mask is None:
		__lowerCAmelCase         =							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       a		(   __UpperCAmelCase      ,	__UpperCAmelCase      ,	unittest.TestCase					):
							lowercase_		:						Any   							=  (
							    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
							)
							lowercase_		:						Tuple   							=  (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
							lowercase_		:						Optional[Any]   							=  (
							    {
							        'conversational': TFBlenderbotSmallForConditionalGeneration,
							        'feature-extraction': TFBlenderbotSmallModel,
							        'summarization': TFBlenderbotSmallForConditionalGeneration,
							        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
							        'translation': TFBlenderbotSmallForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase_		:						str   							=  True
							lowercase_		:						Any   							=  False
							lowercase_		:						int   							=  False







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							TFBlenderbotSmallModelTester(self )
								__lowerCAmelCase         =							ConfigTester(self      ,				config_class=snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								self.config_tester.run_common_tests()







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*snake_case__ )

@require_tokenizers
@require_tf
class       a		(   unittest.TestCase					):
							lowercase_		:						Optional[Any]   							=  [
							    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
							    '  i\'m going to throw up.\nand why is that?'
							]
							lowercase_		:						int   							=  'facebook/blenderbot_small-90M'







							@cached_property
							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )







							@cached_property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
								return model







							@slow
							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.tokenizer(self.src_text      ,				return_tensors="tf" )
								__lowerCAmelCase         =							self.model.generate(
								    model_inputs.input_ids      ,				attention_mask=model_inputs.attention_mask      ,				num_beams=2      ,				use_cache=snake_case__      ,				)
								__lowerCAmelCase         =							self.tokenizer.batch_decode(generated_ids.numpy()      ,				skip_special_tokens=snake_case__ )[0]
								assert generated_words in (
								    "i don't know. i just feel like i'm going to throw up. it's not fun.",
								    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
								    "i'm not sure. i just feel like i've been in a bad situation.",
								)








import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'segformer'







							def __init__(						self  :  List[str]      ,				snake_case__  :  Any=3      ,				snake_case__  :  int=4      ,				snake_case__  :  Tuple=[2, 2, 2, 2]      ,				snake_case__  :  Optional[int]=[8, 4, 2, 1]      ,				snake_case__  :  Union[str, Any]=[32, 64, 160, 256]      ,				snake_case__  :  str=[7, 3, 3, 3]      ,				snake_case__  :  List[Any]=[4, 2, 2, 2]      ,				snake_case__  :  Tuple=[1, 2, 5, 8]      ,				snake_case__  :  List[str]=[4, 4, 4, 4]      ,				snake_case__  :  Optional[Any]="gelu"      ,				snake_case__  :  Optional[Any]=0.0      ,				snake_case__  :  Any=0.0      ,				snake_case__  :  str=0.1      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Any=0.1      ,				snake_case__  :  List[Any]=1E-6      ,				snake_case__  :  Any=256      ,				snake_case__  :  Optional[Any]=255      ,				**snake_case__  :  Union[str, Any]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__ )

								if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
									warnings.warn(
									    "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
									    " removed, as the behaviour will default to that of reshape_last_stage = True."      ,				snake_case__      ,				)

								__lowerCAmelCase         =							num_channels
								__lowerCAmelCase         =							num_encoder_blocks
								__lowerCAmelCase         =							depths
								__lowerCAmelCase         =							sr_ratios
								__lowerCAmelCase         =							hidden_sizes
								__lowerCAmelCase         =							patch_sizes
								__lowerCAmelCase         =							strides
								__lowerCAmelCase         =							mlp_ratios
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							hidden_act
								__lowerCAmelCase         =							hidden_dropout_prob
								__lowerCAmelCase         =							attention_probs_dropout_prob
								__lowerCAmelCase         =							classifier_dropout_prob
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							drop_path_rate
								__lowerCAmelCase         =							layer_norm_eps
								__lowerCAmelCase         =							decoder_hidden_size
								__lowerCAmelCase         =							kwargs.get("reshape_last_stage"      ,				snake_case__ )
								__lowerCAmelCase         =							semantic_loss_ignore_index

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[str]   							=  version.parse('1.11'					)







							@property
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ] )







							@property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return 1E-4







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								return 12






