






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch
@is_pipeline_test
class     lowerCamelCase_					(   unittest.TestCase			):
				a__       							=	MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				a__       							=	TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   TextaTextGenerationPipeline(model=__lowerCAmelCase							,       tokenizer=__lowerCAmelCase  )
								return generator, ["Something to write", "Something else"]







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   generator('''Something there'''  )
								self.assertEqual(__lowerCAmelCase							,       [{'''generated_text''': ANY(__lowerCAmelCase  )}]  )
								# These are encoder decoder, they don't just append to incoming string
								self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''  )  )

								__magic_name__		:Union[str, Any]   =   generator(['''This is great !''', '''Something else''']							,       num_return_sequences=2							,       do_sample=__lowerCAmelCase  )
								self.assertEqual(
								    __lowerCAmelCase							,       [
								        [{'''generated_text''': ANY(__lowerCAmelCase  )}, {'''generated_text''': ANY(__lowerCAmelCase  )}],
								        [{'''generated_text''': ANY(__lowerCAmelCase  )}, {'''generated_text''': ANY(__lowerCAmelCase  )}],
								    ]							,       )

								__magic_name__		:List[str]   =   generator(
								    ['''This is great !''', '''Something else''']							,       num_return_sequences=2							,       batch_size=2							,       do_sample=__lowerCAmelCase  )
								self.assertEqual(
								    __lowerCAmelCase							,       [
								        [{'''generated_text''': ANY(__lowerCAmelCase  )}, {'''generated_text''': ANY(__lowerCAmelCase  )}],
								        [{'''generated_text''': ANY(__lowerCAmelCase  )}, {'''generated_text''': ANY(__lowerCAmelCase  )}],
								    ]							,       )

								with self.assertRaises(__lowerCAmelCase  ):
												generator(4  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   pipeline('''text2text-generation'''							,       model='''patrickvonplaten/t5-tiny-random'''							,       framework='''pt'''  )
								# do_sample=False necessary for reproducibility
								__magic_name__		:Any   =   generator('''Something there'''							,       do_sample=__lowerCAmelCase  )
								self.assertEqual(__lowerCAmelCase							,       [{'''generated_text''': ''''''}]  )

								__magic_name__		:str   =   3
								__magic_name__		:Optional[Any]   =   generator(
								    '''Something there'''							,       num_return_sequences=__lowerCAmelCase							,       num_beams=__lowerCAmelCase							,       )
								__magic_name__		:str   =   [
								    {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
								    {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
								    {'''generated_text''': ''''''},
								]
								self.assertEqual(__lowerCAmelCase							,       __lowerCAmelCase  )

								__magic_name__		:int   =   generator('''This is a test'''							,       do_sample=__lowerCAmelCase							,       num_return_sequences=2							,       return_tensors=__lowerCAmelCase  )
								self.assertEqual(
								    __lowerCAmelCase							,       [
								        {'''generated_token_ids''': ANY(torch.Tensor  )},
								        {'''generated_token_ids''': ANY(torch.Tensor  )},
								    ]							,       )
								__magic_name__		:str   =   generator.model.config.eos_token_id
								__magic_name__		:Dict   =   '''<pad>'''
								__magic_name__		:List[str]   =   generator(
								    ['''This is a test''', '''This is a second test''']							,       do_sample=__lowerCAmelCase							,       num_return_sequences=2							,       batch_size=2							,       return_tensors=__lowerCAmelCase							,       )
								self.assertEqual(
								    __lowerCAmelCase							,       [
								        [
								            {'''generated_token_ids''': ANY(torch.Tensor  )},
								            {'''generated_token_ids''': ANY(torch.Tensor  )},
								        ],
								        [
								            {'''generated_token_ids''': ANY(torch.Tensor  )},
								            {'''generated_token_ids''': ANY(torch.Tensor  )},
								        ],
								    ]							,       )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   pipeline('''text2text-generation'''							,       model='''patrickvonplaten/t5-tiny-random'''							,       framework='''tf'''  )
								# do_sample=False necessary for reproducibility
								__magic_name__		:Union[str, Any]   =   generator('''Something there'''							,       do_sample=__lowerCAmelCase  )
								self.assertEqual(__lowerCAmelCase							,       [{'''generated_text''': ''''''}]  )










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


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[Any]										=						'''mobilenet_v2'''


	def __init__(    self				,     lowercase__=3				,     lowercase__=2_2_4				,     lowercase__=1.0				,     lowercase__=8				,     lowercase__=8				,     lowercase__=6				,     lowercase__=3_2				,     lowercase__=True				,     lowercase__=True				,     lowercase__="relu6"				,     lowercase__=True				,     lowercase__=0.8				,     lowercase__=0.0_2				,     lowercase__=0.0_0_1				,     lowercase__=2_5_5				,     **lowercase__				,     ):
							super().__init__(**lowercase__)

							if depth_multiplier <= 0:
													raise ValueError('''depth_multiplier must be greater than zero.''')

							__UpperCAmelCase			:							Optional[int]	    =	num_channels
							__UpperCAmelCase			:							Optional[Any]	    =	image_size
							__UpperCAmelCase			:							Dict	    =	depth_multiplier
							__UpperCAmelCase			:							Optional[Any]	    =	depth_divisible_by
							__UpperCAmelCase			:							Union[str, Any]	    =	min_depth
							__UpperCAmelCase			:							Dict	    =	expand_ratio
							__UpperCAmelCase			:							int	    =	output_stride
							__UpperCAmelCase			:							Tuple	    =	first_layer_is_expansion
							__UpperCAmelCase			:							Union[str, Any]	    =	finegrained_output
							__UpperCAmelCase			:							int	    =	hidden_act
							__UpperCAmelCase			:							Tuple	    =	tf_padding
							__UpperCAmelCase			:							List[str]	    =	classifier_dropout_prob
							__UpperCAmelCase			:							Optional[Any]	    =	initializer_range
							__UpperCAmelCase			:							List[str]	    =	layer_norm_eps
							__UpperCAmelCase			:							Optional[int]	    =	semantic_loss_ignore_index






class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						version.parse('''1.11''' )


	@property
	def  A(    self):
							return OrderedDict([('''pixel_values''', {0: '''batch'''})])


	@property
	def  A(    self):
							if self.task == "image-classification":
													return OrderedDict([('''logits''', {0: '''batch'''})])
							else:
													return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})])


	@property
	def  A(    self):
							return 1e-4

