



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			:		int		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		Union[str, Any]=13		,						__UpperCAmelCase			:		Tuple=(32, 128)		,						__UpperCAmelCase			:		Any=4		,						__UpperCAmelCase			:		Any=3		,						__UpperCAmelCase			:		int=27		,						__UpperCAmelCase			:		Union[str, Any]=38		,						__UpperCAmelCase			:		int=99		,						__UpperCAmelCase			:		str=99		,						__UpperCAmelCase			:		List[str]=32		,						__UpperCAmelCase			:		Optional[Any]=5		,						__UpperCAmelCase			:		int=4		,						__UpperCAmelCase			:		Union[str, Any]=4.0		,						__UpperCAmelCase			:		List[Any]=257		,						__UpperCAmelCase			:		Any=None		,						)    ->				Optional[Any]:
							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 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Any:
							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 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				List[str]:
							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 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Optional[Any]  )    ->				List[str]:
							UpperCAmelCase_=     MgpstrForSceneTextRecognition(__UpperCAmelCase  )
							model.to(__UpperCAmelCase  )
							model.eval()
							with torch.no_grad():
													UpperCAmelCase_=     model(__UpperCAmelCase  )
							self.parent.assertEqual(
							    generated_ids[0][0].shape		,						(self.batch_size, self.max_token_length, self.num_character_labels)  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				int:
							UpperCAmelCase_=     self.prepare_config_and_inputs()
							UpperCAmelCase_,     UpperCAmelCase_=     config_and_inputs
							UpperCAmelCase_=     {"""pixel_values""": pixel_values}
							return config, inputs_dict






@require_torch
class 	lowercase		(				snake_case__				,					snake_case__				,					unittest.TestCase):






	"""simple docstring"""


	a__   :      Optional[Any]	     =							(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
	a__   :      Tuple	     =							{"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
	a__   :      Optional[Any]	     =							False

	a__   :      int	     =							False
	a__   :      int	     =							False
	a__   :      Union[str, Any]	     =							False
	a__   :      Tuple	     =							False




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				int:
							UpperCAmelCase_=     MgpstrModelTester(self  )
							UpperCAmelCase_=     ConfigTester(self		,						config_class=__UpperCAmelCase		,						has_text_modality=__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Optional[int]:
							self.config_tester.run_common_tests()




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				int:
							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase  )




	@unittest.skip(reason="""MgpstrModel does not use inputs_embeds"""  )
	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				Optional[int]:
							pass




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Tuple:
							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Optional[Any]:
							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													UpperCAmelCase_=     model_class(__UpperCAmelCase  )
													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]		,						__UpperCAmelCase  )




	@unittest.skip(reason="""MgpstrModel does not support feedforward chunking"""  )
	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Any:
							pass




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Any:
							UpperCAmelCase_,     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(__UpperCAmelCase  )
													self.assertTrue(model.is_gradient_checkpointing  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				List[Any]:
							def check_hidden_states_output(__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Optional[Any]  ):
													UpperCAmelCase_=     model_class(__UpperCAmelCase  )
													model.to(__UpperCAmelCase  )
													model.eval()

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

													UpperCAmelCase_=     outputs.hidden_states

													UpperCAmelCase_=     getattr(
													    self.model_tester		,						"""expected_num_hidden_layers"""		,						self.model_tester.num_hidden_layers + 1  )
													self.assertEqual(len(__UpperCAmelCase  )		,						__UpperCAmelCase  )

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

							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()

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

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

													check_hidden_states_output(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				Optional[Any]:
							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()

							UpperCAmelCase_=     _config_zero_init(__UpperCAmelCase  )
							for model_class in self.all_model_classes:
													UpperCAmelCase_=     model_class(config=__UpperCAmelCase  )
													for name, param in model.named_parameters():
																			if isinstance(__UpperCAmelCase		,						(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 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				str:
							pass



def  __a  (					)   ->				int:






						'''simple docstring'''



						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 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Optional[Any]:
							UpperCAmelCase_=     """alibaba-damo/mgp-str-base"""
							UpperCAmelCase_=     MgpstrForSceneTextRecognition.from_pretrained(__UpperCAmelCase  ).to(__UpperCAmelCase  )
							UpperCAmelCase_=     MgpstrProcessor.from_pretrained(__UpperCAmelCase  )

							UpperCAmelCase_=     prepare_img()
							UpperCAmelCase_=     processor(images=__UpperCAmelCase		,						return_tensors="""pt"""  ).pixel_values.to(__UpperCAmelCase  )

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

							# 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]		,						__UpperCAmelCase  )

							UpperCAmelCase_=     torch.tensor(
							    [[[-39.5_397, -44.4_024, -36.1_844], [-61.4_709, -63.8_639, -58.3_454], [-74.0_225, -68.5_494, -71.2_164]]]		,						device=__UpperCAmelCase		,						)

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









import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


__A       					=		logging.get_logger(__name__)






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Optional[int]	     =							"semantic"




	def __init__(			self			:		Any		,						__UpperCAmelCase			:		List[str]=10_000		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		int=768		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=False		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		int=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		int=10_048		,						__UpperCAmelCase			:		Dict=129_595		,						__UpperCAmelCase			:		Optional[Any]=129_599		,						__UpperCAmelCase			:		Tuple=10_000		,						__UpperCAmelCase			:		List[Any]=256		,						__UpperCAmelCase			:		Union[str, Any]=49.9		,						**__UpperCAmelCase			:		Optional[Any]		,						)    ->				Tuple:
							super().__init__(
							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						eos_token_id=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						max_new_tokens=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)

							UpperCAmelCase_=     text_encoding_offset
							UpperCAmelCase_=     text_pad_token
							UpperCAmelCase_=     eos_token_id
							UpperCAmelCase_=     semantic_infer_token
							UpperCAmelCase_=     semantic_vocab_size
							UpperCAmelCase_=     max_input_semantic_length
							UpperCAmelCase_=     semantic_rate_hz






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Any	     =							"coarse_acoustics"




	def __init__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		Union[str, Any]=False		,						__UpperCAmelCase			:		List[str]=False		,						__UpperCAmelCase			:		Any=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Optional[int]=12_048		,						__UpperCAmelCase			:		Optional[Any]=75		,						__UpperCAmelCase			:		str=2		,						__UpperCAmelCase			:		Optional[int]=12_050		,						__UpperCAmelCase			:		Union[str, Any]=256		,						__UpperCAmelCase			:		int = 630		,						__UpperCAmelCase			:		int = 60		,						**__UpperCAmelCase			:		int		,						)    ->				Union[str, Any]:
							super().__init__(
							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)

							UpperCAmelCase_=     coarse_semantic_pad_token
							UpperCAmelCase_=     coarse_rate_hz
							UpperCAmelCase_=     n_coarse_codebooks
							UpperCAmelCase_=     coarse_infer_token
							UpperCAmelCase_=     max_coarse_input_length
							UpperCAmelCase_=     max_coarse_history
							UpperCAmelCase_=     sliding_window_len






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Tuple	     =							"fine_acoustics"




	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		Tuple=0.5		,						__UpperCAmelCase			:		Optional[Any]=512		,						__UpperCAmelCase			:		Optional[Any]=1_024		,						__UpperCAmelCase			:		List[str]=8		,						**__UpperCAmelCase			:		str		,						)    ->				Tuple:
							super().__init__(temperature=__UpperCAmelCase  )

							UpperCAmelCase_=     max_fine_history_length
							UpperCAmelCase_=     max_fine_input_length
							UpperCAmelCase_=     n_fine_codebooks






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Any	     =							"bark"
	a__   :      List[str]	     =							True




	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		str=24_000		,						__UpperCAmelCase			:		List[str]=1_024		,						**__UpperCAmelCase			:		Optional[int]		,						)    ->				List[str]:
							if semantic_config is None:
													UpperCAmelCase_=     {}
													logger.info("""semantic_config is None. initializing the semantic model with default values."""  )

							if coarse_acoustics_config is None:
													UpperCAmelCase_=     {}
													logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""  )

							if fine_acoustics_config is None:
													UpperCAmelCase_=     {}
													logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""  )

							UpperCAmelCase_=     BarkSemanticGenerationConfig(**__UpperCAmelCase  )
							UpperCAmelCase_=     BarkCoarseGenerationConfig(**__UpperCAmelCase  )
							UpperCAmelCase_=     BarkFineGenerationConfig(**__UpperCAmelCase  )

							UpperCAmelCase_=     sample_rate
							UpperCAmelCase_=     codebook_size




	@classmethod
	def 						_SCREAMING_SNAKE_CASE	(			cls			:		str		,						__UpperCAmelCase			:		BarkSemanticGenerationConfig		,						__UpperCAmelCase			:		BarkCoarseGenerationConfig		,						__UpperCAmelCase			:		BarkFineGenerationConfig		,						**__UpperCAmelCase			:		Any		,						)    ->				Any:
							return cls(
							    semantic_config=semantic_config.to_dict()		,						coarse_acoustics_config=coarse_acoustics_config.to_dict()		,						fine_acoustics_config=fine_acoustics_config.to_dict()		,						**__UpperCAmelCase		,						)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Optional[Any]:
							UpperCAmelCase_=     copy.deepcopy(self.__dict__  )

							UpperCAmelCase_=     self.semantic_config.to_dict()
							UpperCAmelCase_=     self.coarse_acoustics_config.to_dict()
							UpperCAmelCase_=     self.fine_acoustics_config.to_dict()

							UpperCAmelCase_=     self.__class__.model_type
							return output




