



import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM



@require_torch
class     _UpperCAmelCase			:




     def __init__(				self ,						a__ ,						a__=9_9 ,						a__=1_3 ,						a__=1_6 ,						a__=7 ,						a__=True ,						a__=True ,						a__=True ,						a__=False ,						a__=True ,						a__=2 ,						a__=3_2 ,						a__=4 ,						a__=4 ,						a__=3_0 ,						a__=0 ,						a__=1 ,						a__=2 ,						a__=None ,						):
         A__		 =					parent
         A__		 =					batch_size
         A__		 =					decoder_seq_length
         # For common tests
         A__		 =					self.decoder_seq_length
         A__		 =					is_training
         A__		 =					use_attention_mask
         A__		 =					use_labels

         A__		 =					vocab_size
         A__		 =					d_model
         A__		 =					d_model
         A__		 =					decoder_layers
         A__		 =					decoder_layers
         A__		 =					decoder_ffn_dim
         A__		 =					decoder_attention_heads
         A__		 =					decoder_attention_heads
         A__		 =					eos_token_id
         A__		 =					bos_token_id
         A__		 =					pad_token_id
         A__		 =					decoder_start_token_id
         A__		 =					use_cache
         A__		 =					max_position_embeddings

         A__		 =					None
         A__		 =					decoder_seq_length
         A__		 =					2
         A__		 =					1




     def 		snake_case_		(				self):
         A__		 =					ids_tensor([self.batch_size, self.decoder_seq_length] ,						self.vocab_size)

         A__		 =					None
         if self.use_attention_mask:
             A__		 =					ids_tensor([self.batch_size, self.decoder_seq_length] ,						vocab_size=2)

         A__		 =					None
         if self.use_labels:
             A__		 =					ids_tensor([self.batch_size, self.decoder_seq_length] ,						self.vocab_size)

         A__		 =					TrOCRConfig(
             vocab_size=self.vocab_size ,						d_model=self.d_model ,						decoder_layers=self.decoder_layers ,						decoder_ffn_dim=self.decoder_ffn_dim ,						decoder_attention_heads=self.decoder_attention_heads ,						eos_token_id=self.eos_token_id ,						bos_token_id=self.bos_token_id ,						use_cache=self.use_cache ,						pad_token_id=self.pad_token_id ,						decoder_start_token_id=self.decoder_start_token_id ,						max_position_embeddings=self.max_position_embeddings ,						)

         return (config, input_ids, attention_mask, lm_labels)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					True
         A__		 =					TrOCRDecoder(config=a__).to(a__).eval()
         A__		 =					input_ids[:2]

         input_ids[input_ids == 0] += 1
         # first forward pass
         A__		 =					model(a__ ,						use_cache=a__)
         A__		 =					model(a__)
         A__		 =					model(a__ ,						use_cache=a__)

         self.parent.assertTrue(len(a__) == len(a__))
         self.parent.assertTrue(len(a__) == len(a__) + 1)

         A__		 =					outputs['''past_key_values''']

         # create hypothetical next token and extent to next_input_ids
         A__		 =					ids_tensor((2, 1) ,						config.vocab_size - 1) + 1

         # append to next input_ids and
         A__		 =					torch.cat([input_ids, next_tokens] ,						dim=-1)

         A__		 =					model(a__)['''last_hidden_state''']
         A__		 =					model(a__ ,						past_key_values=a__)['''last_hidden_state''']

         # select random slice
         A__		 =					ids_tensor((1,) ,						output_from_past.shape[-1]).item()
         A__		 =					output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
         A__		 =					output_from_past[:, 0, random_slice_idx].detach()

         # test that outputs are equal for slice
         assert torch.allclose(a__ ,						a__ ,						atol=1e-3)




     def 		snake_case_		(				self):
         A__		 =					self.prepare_config_and_inputs()
         A__ ,	A__ ,	A__ ,	A__		 =					config_and_inputs

         A__		 =					{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict







@require_torch
class     _UpperCAmelCase			(       A__			,      A__			,      A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
     UpperCamelCase__		    =				(TrOCRForCausalLM,) if is_torch_available() else ()
     UpperCamelCase__		    =				{'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
     UpperCamelCase__		    =				True
     UpperCamelCase__		    =				False




     def 		snake_case_		(				self):
         A__		 =					TrOCRStandaloneDecoderModelTester(self ,						is_training=a__)
         A__		 =					ConfigTester(self ,						config_class=a__)




     def 		snake_case_		(				self):
         pass




     def 		snake_case_		(				self):
         pass




     def 		snake_case_		(				self):
         pass




     def 		snake_case_		(				self):
         self.config_tester.run_common_tests()




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past(*a__)




     def 		snake_case_		(				self):
         return




     @unittest.skip('''The model doesn\'t support left padding''')  # and it's not used enough to be worth fixing :)
     def 		snake_case_		(				self):
         pass








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_lowercase						  =		{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		["MobileViTFeatureExtractor"]
   _lowercase						  =		["MobileViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MobileViTForImageClassification",
       "MobileViTForSemanticSegmentation",
       "MobileViTModel",
       "MobileViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFMobileViTForImageClassification",
       "TFMobileViTForSemanticSegmentation",
       "TFMobileViTModel",
       "TFMobileViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilevit import MobileViTFeatureExtractor
      from .image_processing_mobilevit import MobileViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilevit import (
          MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileViTForImageClassification,
          MobileViTForSemanticSegmentation,
          MobileViTModel,
          MobileViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mobilevit import (
          TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMobileViTForImageClassification,
          TFMobileViTForSemanticSegmentation,
          TFMobileViTModel,
          TFMobileViTPreTrainedModel,
      )


else:
   import sys

   _lowercase						  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
