import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
     from PIL import Image

     from transformers import MgpstrProcessor

class   _lowercase:



   """simple docstring"""





   def __init__(							self:    Optional[int]			,a:    List[str]			,a:    Any=False			,a:    List[str]=13			,a:    List[Any]=(32, 128)			,a:    Optional[int]=4			,a:    Optional[Any]=3			,a:    Dict=27			,a:    List[Any]=38			,a:    Optional[int]=99			,a:    Union[str, Any]=99			,a:    Optional[int]=32			,a:    Optional[int]=5			,a:    Dict=4			,a:    str=4.0			,a:    Dict=257			,a:    List[str]=None			,):
    __UpperCAmelCase         =					parent
    __UpperCAmelCase         =					is_training
    __UpperCAmelCase         =					batch_size
    __UpperCAmelCase         =					image_size
    __UpperCAmelCase         =					patch_size
    __UpperCAmelCase         =					num_channels
    __UpperCAmelCase         =					max_token_length
    __UpperCAmelCase         =					num_character_labels
    __UpperCAmelCase         =					num_bpe_labels
    __UpperCAmelCase         =					num_wordpiece_labels
    __UpperCAmelCase         =					hidden_size
    __UpperCAmelCase         =					num_hidden_layers
    __UpperCAmelCase         =					num_attention_heads
    __UpperCAmelCase         =					mlp_ratio
    __UpperCAmelCase         =					patch_embeds_hidden_size
    __UpperCAmelCase         =					output_hidden_states





   def 			snake_case	(							self:    List[str]	):
    __UpperCAmelCase         =					floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]	)
    __UpperCAmelCase         =					self.get_config()
    return config, pixel_values





   def 			snake_case	(							self:    Any	):
    return MgpstrConfig(
        image_size=self.image_size			,patch_size=self.patch_size			,num_channels=self.num_channels			,max_token_length=self.max_token_length			,num_character_labels=self.num_character_labels			,num_bpe_labels=self.num_bpe_labels			,num_wordpiece_labels=self.num_wordpiece_labels			,hidden_size=self.hidden_size			,num_hidden_layers=self.num_hidden_layers			,num_attention_heads=self.num_attention_heads			,mlp_ratio=self.mlp_ratio			,output_hidden_states=self.output_hidden_states			,)





   def 			snake_case	(							self:    str			,a:    List[Any]			,a:    List[Any]	):
    __UpperCAmelCase         =					MgpstrForSceneTextRecognition(a	)
    model.to(a	)
    model.eval()
    with torch.no_grad():
     __UpperCAmelCase         =					model(a	)
    self.parent.assertEqual(
        generated_ids[0][0].shape			,(self.batch_size, self.max_token_length, self.num_character_labels)	)





   def 			snake_case	(							self:    List[str]	):
    __UpperCAmelCase         =					self.prepare_config_and_inputs()
    __UpperCAmelCase         =					config_and_inputs
    __UpperCAmelCase         =					{"pixel_values": pixel_values}
    return config, inputs_dict




@require_torch
class   _lowercase(     _lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase				):



   """simple docstring"""





   __lowerCamelCase    =  (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
   __lowerCamelCase    =  {"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
   __lowerCamelCase    =  False

   __lowerCamelCase    =  False
   __lowerCamelCase    =  False
   __lowerCamelCase    =  False
   __lowerCamelCase    =  False





   def 			snake_case	(							self:    Optional[Any]	):
    __UpperCAmelCase         =					MgpstrModelTester(self	)
    __UpperCAmelCase         =					ConfigTester(self			,config_class=a			,has_text_modality=a	)





   def 			snake_case	(							self:    Optional[Any]	):
    self.config_tester.run_common_tests()





   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a	)





   @unittest.skip(reason='MgpstrModel does not use inputs_embeds'	)
   def 			snake_case	(							self:    Optional[Any]	):
    pass





   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     __UpperCAmelCase         =					model_class(a	)
     self.assertIsInstance(model.get_input_embeddings()			,(nn.Module)	)
     __UpperCAmelCase         =					model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(a			,nn.Linear	)	)





   def 			snake_case	(							self:    List[str]	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     __UpperCAmelCase         =					model_class(a	)
     __UpperCAmelCase         =					inspect.signature(model.forward	)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __UpperCAmelCase         =					[*signature.parameters.keys()]

     __UpperCAmelCase         =					["pixel_values"]
     self.assertListEqual(arg_names[:1]			,a	)





   @unittest.skip(reason='MgpstrModel does not support feedforward chunking'	)
   def 			snake_case	(							self:    Union[str, Any]	):
    pass





   def 			snake_case	(							self:    Union[str, Any]	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     if not model_class.supports_gradient_checkpointing:
      continue

     __UpperCAmelCase         =					True
     __UpperCAmelCase         =					model_class(a	)
     self.assertTrue(model.is_gradient_checkpointing	)





   def 			snake_case	(							self:    Union[str, Any]	):






    def check_hidden_states_output(a:    Optional[Any]			,a:    Any			,a:    List[Any]	):
     __UpperCAmelCase         =					model_class(a	)
     model.to(a	)
     model.eval()

     with torch.no_grad():
      __UpperCAmelCase         =					model(**self._prepare_for_class(a			,a	)	)

     __UpperCAmelCase         =					outputs.hidden_states

     __UpperCAmelCase         =					getattr(
         self.model_tester			,'expected_num_hidden_layers'			,self.model_tester.num_hidden_layers + 1	)
     self.assertEqual(len(a	)			,a	)

     self.assertListEqual(
         list(hidden_states[0].shape[-2:]	)			,[self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]			,)

    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     __UpperCAmelCase         =					True
     check_hidden_states_output(a			,a			,a	)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     __UpperCAmelCase         =					True

     check_hidden_states_output(a			,a			,a	)





   def 			snake_case	(							self:    Optional[int]	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

    __UpperCAmelCase         =					_config_zero_init(a	)
    for model_class in self.all_model_classes:
     __UpperCAmelCase         =					model_class(config=a	)
     for name, param in model.named_parameters():
      if isinstance(a			,(nn.Linear, nn.Convad, nn.LayerNorm)	):
       if param.requires_grad:
        self.assertIn(
            ((param.data.mean() * 1e9).round() / 1e9).item()			,[0.0, 1.0]			,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""			,)





   @unittest.skip(reason='Retain_grad is tested in individual model tests'	)
   def 			snake_case	(							self:    Any	):
    pass




def 						__snake_case (       ):
 __UpperCAmelCase         =					"https://i.postimg.cc/ZKwLg2Gw/367-14.png"
 __UpperCAmelCase         =					Image.open(requests.get(lowerCAmelCase      ,  stream=lowerCAmelCase			).raw			).convert('RGB'			)
 return im

@require_vision
@require_torch
class   _lowercase(     unittest.TestCase				):



   """simple docstring"""





   @slow
   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase         =					"alibaba-damo/mgp-str-base"
    __UpperCAmelCase         =					MgpstrForSceneTextRecognition.from_pretrained(a	).to(a	)
    __UpperCAmelCase         =					MgpstrProcessor.from_pretrained(a	)

    __UpperCAmelCase         =					prepare_img()
    __UpperCAmelCase         =					processor(images=a			,return_tensors='pt'	).pixel_values.to(a	)

    # forward pass
    with torch.no_grad():
     __UpperCAmelCase         =					model(a	)

    # verify the logits
    self.assertEqual(outputs.logits[0].shape			,torch.Size((1, 27, 38)	)	)

    __UpperCAmelCase         =					processor.batch_decode(outputs.logits	)
    __UpperCAmelCase         =					"ticket"

    self.assertEqual(out_strs['generated_text'][0]			,a	)

    __UpperCAmelCase         =					torch.tensor(
        [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]			,device=a			,)

    self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]			,a			,atol=1e-4	)	)




'''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,
)


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		['MarianTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		[
         'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MarianForCausalLM',
         'MarianModel',
         'MarianMTModel',
         'MarianPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     _UpperCamelCase				:		List[Any]       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



