
'''simple docstring'''
from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

lowerCAmelCase					:    List[str]           = logging.get_logger(__name__)


@add_end_docstrings(snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				*A_						,				**A_     )->		str:







											'''simple docstring'''






											super().__init__(*A_						,				**A_     )
											requires_backends(self						,				'vision'     )
											self.check_model_type(A_     )







				def __call__(       self						,				A_						,				**A_     )->		Any:







											'''simple docstring'''






											return super().__call__(A_						,				**A_     )







				def 						UpperCAmelCase_    (       self						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											return {}, {}, {}







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     load_image(A_     )
											UpperCamelCase   			=     image.size
											UpperCamelCase   			=     self.image_processor(images=A_						,				return_tensors=self.framework     )
											return model_inputs







				def 						UpperCAmelCase_    (       self						,				A_     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.model(**A_     )
											return model_outputs







				def 						UpperCAmelCase_    (       self						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     model_outputs.predicted_depth
											UpperCamelCase   			=     torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1     )						,				size=self.image_size[::-1]						,				mode='bicubic'						,				align_corners=A_     )
											UpperCamelCase   			=     prediction.squeeze().cpu().numpy()
											UpperCamelCase   			=     (output * 255 / np.max(A_     )).astype('uint8'     )
											UpperCamelCase   			=     Image.fromarray(A_     )
											UpperCamelCase   			=     {}
											UpperCamelCase   			=     predicted_depth
											UpperCamelCase   			=     depth
											return output_dict






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase					:    Tuple           = TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase					:    Optional[int]           = TaTokenizerFast

lowerCAmelCase					:    Any           = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[int]           = [
						    'MT5EncoderModel',
						    'MT5ForConditionalGeneration',
						    'MT5ForQuestionAnswering',
						    'MT5Model',
						    'MT5PreTrainedModel',
						    'MT5Stack',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Dict           = ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[Any]           = ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase					:    Tuple           = _LazyModule(
						    __name__,
						    globals()['__file__'],
						    _import_structure,
						    extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
						    module_spec=__spec__,
						)




