






"""simple docstring"""


import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY






@is_pipeline_test
class 						snake_case  (  unittest.TestCase       ):
    __magic_name__         =     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    __magic_name__         =     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					Union[str, Any]				,		A	:					Tuple				,		A	:					Any			):


      '''simple docstring'''




      if isinstance(model.config				,		A			):
        a,     a     :			int				=   list(tokenizer.lang_code_to_id.keys()			)[:2]
        a     :			Dict				=   TranslationPipeline(model=A				,		tokenizer=A				,		src_lang=A				,		tgt_lang=A			)
      else:
        a     :			List[str]				=   TranslationPipeline(model=A				,		tokenizer=A			)
      return translator, ["Some string", "Some other text"]





    def 				lowerCamelCase__      (							self	:					int				,		A	:					int				,		A	:					Tuple			):


      '''simple docstring'''




      a     :			Tuple				=   translator('Some string'			)
      self.assertEqual(A				,		[{'translation_text': ANY(A			)}]			)

      a     :			Optional[int]				=   translator(['Some string']			)
      self.assertEqual(A				,		[{'translation_text': ANY(A			)}]			)

      a     :			List[str]				=   translator(['Some string', 'other string']			)
      self.assertEqual(A				,		[{'translation_text': ANY(A			)}, {'translation_text': ANY(A			)}]			)





    @require_torch
    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			List[Any]				=   pipeline('translation_en_to_ro'				,		model='patrickvonplaten/t5-tiny-random'				,		framework='pt'			)
      a     :			str				=   translator('This is a test string'				,		max_length=2_0			)
      self.assertEqual(
          A				,		[
              {
                  'translation_text': (
                      'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                      ' Beide Beide'
                  )
              }
          ]				,		)





    @require_tf
    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			Union[str, Any]				=   pipeline('translation_en_to_ro'				,		model='patrickvonplaten/t5-tiny-random'				,		framework='tf'			)
      a     :			Tuple				=   translator('This is a test string'				,		max_length=2_0			)
      self.assertEqual(
          A				,		[
              {
                  'translation_text': (
                      'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                      ' Beide Beide'
                  )
              }
          ]				,		)





    @require_torch
    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      a     :			str				=   pipeline('translation_en_to_de'				,		model='patrickvonplaten/t5-tiny-random'				,		framework='pt'			)
      a     :			List[str]				=   translator('This is a test string'				,		max_length=2_0			)
      self.assertEqual(
          A				,		[
              {
                  'translation_text': (
                      'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                      ' urine urine urine urine urine urine urine'
                  )
              }
          ]				,		)





    @require_tf
    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      a     :			Dict				=   pipeline('translation_en_to_de'				,		model='patrickvonplaten/t5-tiny-random'				,		framework='tf'			)
      a     :			Union[str, Any]				=   translator('This is a test string'				,		max_length=2_0			)
      self.assertEqual(
          A				,		[
              {
                  'translation_text': (
                      'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                      ' urine urine urine urine urine urine urine'
                  )
              }
          ]				,		)






class 						snake_case  (  unittest.TestCase       ):





    @require_torch
    @slow
    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      with self.assertRaises(A			):
        pipeline(task='translation_cn_to_ar'			)

      # but we do for this one
      a     :			List[Any]				=   pipeline(task='translation_en_to_de'			)
      self.assertEqual(translator._preprocess_params['src_lang']				,		'en'			)
      self.assertEqual(translator._preprocess_params['tgt_lang']				,		'de'			)





    @require_torch
    @slow
    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      a     :			Optional[int]				=   MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'			)
      a     :			int				=   MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'			)

      a     :			Optional[Any]				=   pipeline(task='translation'				,		model=A				,		tokenizer=A			)
      # Missing src_lang, tgt_lang
      with self.assertRaises(A			):
        translator('This is a test'			)

      a     :			int				=   translator('This is a test'				,		src_lang='en_XX'				,		tgt_lang='ar_AR'			)
      self.assertEqual(A				,		[{'translation_text': 'هذا إختبار'}]			)

      a     :			Optional[int]				=   translator('This is a test'				,		src_lang='en_XX'				,		tgt_lang='hi_IN'			)
      self.assertEqual(A				,		[{'translation_text': 'यह एक परीक्षण है'}]			)

      # src_lang, tgt_lang can be defined at pipeline call time
      a     :			Any				=   pipeline(task='translation'				,		model=A				,		tokenizer=A				,		src_lang='en_XX'				,		tgt_lang='ar_AR'			)
      a     :			int				=   translator('This is a test'			)
      self.assertEqual(A				,		[{'translation_text': 'هذا إختبار'}]			)





    @require_torch
    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			int				=   'patrickvonplaten/t5-tiny-random'
      a     :			Optional[int]				=   pipeline(task='translation_cn_to_ar'				,		model=A			)
      self.assertEqual(translator._preprocess_params['src_lang']				,		'cn'			)
      self.assertEqual(translator._preprocess_params['tgt_lang']				,		'ar'			)





    @require_torch
    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			str				=   'patrickvonplaten/t5-tiny-random'
      with pytest.warns(A				,		match=R'.*translation_en_to_de.*'			):
        a     :			Optional[Any]				=   pipeline(task='translation'				,		model=A			)
      self.assertEqual(translator.task				,		'translation_en_to_de'			)
      self.assertEqual(translator._preprocess_params['src_lang']				,		'en'			)
      self.assertEqual(translator._preprocess_params['tgt_lang']				,		'de'			)





    @require_torch
    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      with self.assertRaises(A			):
        pipeline(task='translation'			)







'''simple docstring'''






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


snake_case_       =       logging.get_logger(__name__)

snake_case_       =       {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							"mobilevit"




    def __init__(					self				,     lowercase__=3				,     lowercase__=256				,     lowercase__=2				,     lowercase__=[144, 192, 240]				,     lowercase__=[16, 32, 64, 96, 128, 160, 640]				,     lowercase__=4				,     lowercase__=2.0				,     lowercase__=4.0				,     lowercase__="silu"				,     lowercase__=3				,     lowercase__=32				,     lowercase__=0.1				,     lowercase__=0.0				,     lowercase__=0.1				,     lowercase__=0.02				,     lowercase__=1e-5				,     lowercase__=True				,     lowercase__=256				,     lowercase__=[6, 12, 18]				,     lowercase__=0.1				,     lowercase__=255				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(**lowercase__			)

          SCREAMING_SNAKE_CASE_      :			int     =       num_channels
          SCREAMING_SNAKE_CASE_      :			List[str]     =       image_size
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       patch_size
          SCREAMING_SNAKE_CASE_      :			List[str]     =       hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       neck_hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       num_attention_heads
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       mlp_ratio
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       expand_ratio
          SCREAMING_SNAKE_CASE_      :			str     =       hidden_act
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       conv_kernel_size
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       output_stride
          SCREAMING_SNAKE_CASE_      :			Tuple     =       hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :			int     =       attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Tuple     =       classifier_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       initializer_range
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       layer_norm_eps
          SCREAMING_SNAKE_CASE_      :			str     =       qkv_bias

          # decode head attributes for semantic segmentation
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       aspp_out_channels
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       atrous_rates
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       aspp_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Any     =       semantic_loss_ignore_index






class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							version.parse("1.11"      )




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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







    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 1e-4

