
import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertWithLMHeadModel,
    )
    from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST




class 				_lowerCamelCase (      SCREAMING_SNAKE_CASE__ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1_3						,						__SCREAMING_SNAKE_CASE=7						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=9_9						,						__SCREAMING_SNAKE_CASE=0						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=5						,						__SCREAMING_SNAKE_CASE=4						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=5_1_2						,						__SCREAMING_SNAKE_CASE=1_2						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=4						,						__SCREAMING_SNAKE_CASE="last"						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						)  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				parent
           UpperCamelCase__    :   Tuple					=				batch_size
           UpperCamelCase__    :   Tuple					=				seq_length
           UpperCamelCase__    :   str					=				is_training
           UpperCamelCase__    :   str					=				use_input_lengths
           UpperCamelCase__    :   Any					=				use_token_type_ids
           UpperCamelCase__    :   List[Any]					=				use_labels
           UpperCamelCase__    :   Optional[int]					=				gelu_activation
           UpperCamelCase__    :   str					=				sinusoidal_embeddings
           UpperCamelCase__    :   List[str]					=				causal
           UpperCamelCase__    :   Any					=				asm
           UpperCamelCase__    :   Optional[int]					=				n_langs
           UpperCamelCase__    :   Union[str, Any]					=				vocab_size
           UpperCamelCase__    :   int					=				n_special
           UpperCamelCase__    :   Any					=				hidden_size
           UpperCamelCase__    :   int					=				num_hidden_layers
           UpperCamelCase__    :   List[str]					=				num_attention_heads
           UpperCamelCase__    :   List[str]					=				hidden_dropout_prob
           UpperCamelCase__    :   str					=				attention_probs_dropout_prob
           UpperCamelCase__    :   Any					=				max_position_embeddings
           UpperCamelCase__    :   List[Any]					=				type_vocab_size
           UpperCamelCase__    :   int					=				type_sequence_label_size
           UpperCamelCase__    :   Any					=				initializer_range
           UpperCamelCase__    :   Optional[int]					=				num_labels
           UpperCamelCase__    :   Union[str, Any]					=				num_choices
           UpperCamelCase__    :   List[Any]					=				summary_type
           UpperCamelCase__    :   Optional[int]					=				use_proj
           UpperCamelCase__    :   int					=				scope






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size  )
           UpperCamelCase__    :   Dict					=				random_attention_mask([self.batch_size, self.seq_length]  )

           UpperCamelCase__    :   Tuple					=				None
           if self.use_input_lengths:
                 UpperCamelCase__    :   List[Any]					=				(
                     ids_tensor([self.batch_size]						,						vocab_size=2  ) + self.seq_length - 2
                 )  # small variation of seq_length

           UpperCamelCase__    :   Tuple					=				None
           if self.use_token_type_ids:
                 UpperCamelCase__    :   Any					=				ids_tensor([self.batch_size, self.seq_length]						,						self.n_langs  )

           UpperCamelCase__    :   str					=				None
           UpperCamelCase__    :   Tuple					=				None
           UpperCamelCase__    :   Dict					=				None
           if self.use_labels:
                 UpperCamelCase__    :   Optional[Any]					=				ids_tensor([self.batch_size]						,						self.type_sequence_label_size  )
                 UpperCamelCase__    :   Optional[Any]					=				ids_tensor([self.batch_size, self.seq_length]						,						self.num_labels  )
                 UpperCamelCase__    :   Tuple					=				ids_tensor([self.batch_size]						,						2  ).float()
                 UpperCamelCase__    :   Tuple					=				ids_tensor([self.batch_size]						,						self.num_choices  )

           UpperCamelCase__    :   List[Any]					=				self.get_config()

           return (
               config,
               input_ids,
               token_type_ids,
               input_lengths,
               sequence_labels,
               token_labels,
               is_impossible_labels,
               choice_labels,
               input_mask,
           )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           return FlaubertConfig(
               vocab_size=self.vocab_size						,						n_special=self.n_special						,						emb_dim=self.hidden_size						,						n_layers=self.num_hidden_layers						,						n_heads=self.num_attention_heads						,						dropout=self.hidden_dropout_prob						,						attention_dropout=self.attention_probs_dropout_prob						,						gelu_activation=self.gelu_activation						,						sinusoidal_embeddings=self.sinusoidal_embeddings						,						asm=self.asm						,						causal=self.causal						,						n_langs=self.n_langs						,						max_position_embeddings=self.max_position_embeddings						,						initializer_range=self.initializer_range						,						summary_type=self.summary_type						,						use_proj=self.use_proj						,						)






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						)  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				FlaubertModel(config=snake_case__  )
           model.to(snake_case__  )
           model.eval()
           UpperCamelCase__    :   str					=				model(snake_case__						,						lengths=snake_case__						,						langs=snake_case__  )
           UpperCamelCase__    :   str					=				model(snake_case__						,						langs=snake_case__  )
           UpperCamelCase__    :   Any					=				model(snake_case__  )
           self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						)  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   str					=				FlaubertWithLMHeadModel(snake_case__  )
           model.to(snake_case__  )
           model.eval()

           UpperCamelCase__    :   Optional[Any]					=				model(snake_case__						,						token_type_ids=snake_case__						,						labels=snake_case__  )
           self.parent.assertEqual(result.loss.shape						,						()  )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.vocab_size)  )






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




           """simple docstring"""







           UpperCamelCase__    :   str					=				FlaubertForQuestionAnsweringSimple(snake_case__  )
           model.to(snake_case__  )
           model.eval()

           UpperCamelCase__    :   List[str]					=				model(snake_case__  )

           UpperCamelCase__    :   Dict					=				model(snake_case__						,						start_positions=snake_case__						,						end_positions=snake_case__  )
           self.parent.assertEqual(result.start_logits.shape						,						(self.batch_size, self.seq_length)  )
           self.parent.assertEqual(result.end_logits.shape						,						(self.batch_size, self.seq_length)  )






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




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				FlaubertForQuestionAnswering(snake_case__  )
           model.to(snake_case__  )
           model.eval()

           UpperCamelCase__    :   Dict					=				model(snake_case__  )

           UpperCamelCase__    :   Any					=				model(
               snake_case__						,						start_positions=snake_case__						,						end_positions=snake_case__						,						cls_index=snake_case__						,						is_impossible=snake_case__						,						p_mask=snake_case__						,						)

           UpperCamelCase__    :   List[str]					=				model(
               snake_case__						,						start_positions=snake_case__						,						end_positions=snake_case__						,						cls_index=snake_case__						,						is_impossible=snake_case__						,						)

           (UpperCamelCase__			)    :   Optional[Any]					=				result_with_labels.to_tuple()

           UpperCamelCase__    :   Union[str, Any]					=				model(snake_case__						,						start_positions=snake_case__						,						end_positions=snake_case__  )

           (UpperCamelCase__			)    :   List[Any]					=				result_with_labels.to_tuple()

           self.parent.assertEqual(result_with_labels.loss.shape						,						()  )
           self.parent.assertEqual(result.start_top_log_probs.shape						,						(self.batch_size, model.config.start_n_top)  )
           self.parent.assertEqual(result.start_top_index.shape						,						(self.batch_size, model.config.start_n_top)  )
           self.parent.assertEqual(
               result.end_top_log_probs.shape						,						(self.batch_size, model.config.start_n_top * model.config.end_n_top)  )
           self.parent.assertEqual(
               result.end_top_index.shape						,						(self.batch_size, model.config.start_n_top * model.config.end_n_top)  )
           self.parent.assertEqual(result.cls_logits.shape						,						(self.batch_size,)  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						)  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				FlaubertForSequenceClassification(snake_case__  )
           model.to(snake_case__  )
           model.eval()

           UpperCamelCase__    :   Optional[Any]					=				model(snake_case__  )
           UpperCamelCase__    :   Optional[Any]					=				model(snake_case__						,						labels=snake_case__  )

           self.parent.assertEqual(result.loss.shape						,						()  )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)  )






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




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self.num_labels
           UpperCamelCase__    :   Union[str, Any]					=				FlaubertForTokenClassification(snake_case__  )
           model.to(snake_case__  )
           model.eval()

           UpperCamelCase__    :   int					=				model(snake_case__						,						attention_mask=snake_case__						,						labels=snake_case__  )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.num_labels)  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						)  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   int					=				self.num_choices
           UpperCamelCase__    :   str					=				FlaubertForMultipleChoice(config=snake_case__  )
           model.to(snake_case__  )
           model.eval()
           UpperCamelCase__    :   Dict					=				input_ids.unsqueeze(1  ).expand(-1						,						self.num_choices						,						-1  ).contiguous()
           UpperCamelCase__    :   int					=				token_type_ids.unsqueeze(1  ).expand(-1						,						self.num_choices						,						-1  ).contiguous()
           UpperCamelCase__    :   str					=				input_mask.unsqueeze(1  ).expand(-1						,						self.num_choices						,						-1  ).contiguous()
           UpperCamelCase__    :   Any					=				model(
               snake_case__						,						attention_mask=snake_case__						,						token_type_ids=snake_case__						,						labels=snake_case__						,						)
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_choices)  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				self.prepare_config_and_inputs()
           (
               UpperCamelCase__			
           )    :   Any					=				config_and_inputs
           UpperCamelCase__    :   Tuple					=				{
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
               "lengths": input_lengths,
               "attention_mask": input_mask,
           }
           return config, inputs_dict




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





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (
         (
             FlaubertModel,
             FlaubertWithLMHeadModel,
             FlaubertForQuestionAnswering,
             FlaubertForQuestionAnsweringSimple,
             FlaubertForSequenceClassification,
             FlaubertForTokenClassification,
             FlaubertForMultipleChoice,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_		   =  (
         {
             '''feature-extraction''': FlaubertModel,
             '''fill-mask''': FlaubertWithLMHeadModel,
             '''question-answering''': FlaubertForQuestionAnsweringSimple,
             '''text-classification''': FlaubertForSequenceClassification,
             '''token-classification''': FlaubertForTokenClassification,
             '''zero-shot''': FlaubertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           if (
               pipeline_test_casse_name == "QAPipelineTests"
               and tokenizer_name is not None
               and not tokenizer_name.endswith('''Fast'''  )
           ):
                 # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                 # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                 # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                 return True

           return False






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=False  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				super()._prepare_for_class(snake_case__						,						snake_case__						,						return_labels=snake_case__  )

           if return_labels:
                 if model_class.__name__ == "FlaubertForQuestionAnswering":
                       UpperCamelCase__    :   List[Any]					=				torch.zeros(
                           self.model_tester.batch_size						,						dtype=torch.long						,						device=snake_case__  )
                       UpperCamelCase__    :   List[str]					=				torch.zeros(
                           self.model_tester.batch_size						,						dtype=torch.long						,						device=snake_case__  )

           return inputs_dict






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				FlaubertModelTester(self  )
           UpperCamelCase__    :   List[str]					=				ConfigTester(self						,						config_class=snake_case__						,						emb_dim=3_7  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           self.config_tester.run_common_tests()






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_model(*snake_case__  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_lm_head(*snake_case__  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_simple_qa(*snake_case__  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_qa(*snake_case__  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_sequence_classif(*snake_case__  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_token_classif(*snake_case__  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_multiple_choice(*snake_case__  )






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__    :   List[str]					=				FlaubertModel.from_pretrained(snake_case__  )
                 self.assertIsNotNone(snake_case__  )






     @slow
     @require_torch_gpu
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
                 if model_class == FlaubertForMultipleChoice:
                       return

                 UpperCamelCase__    :   int					=				True
                 UpperCamelCase__    :   List[Any]					=				model_class(config=snake_case__  )

                 UpperCamelCase__    :   str					=				self._prepare_for_class(snake_case__						,						snake_case__  )
                 UpperCamelCase__    :   Dict					=				torch.jit.trace(
                     snake_case__						,						(inputs_dict['''input_ids'''].to('''cpu'''  ), inputs_dict['''attention_mask'''].to('''cpu'''  ))  )

                 with tempfile.TemporaryDirectory() as tmp:
                       torch.jit.save(snake_case__						,						os.path.join(snake_case__						,						'''traced_model.pt'''  )  )
                       UpperCamelCase__    :   str					=				torch.jit.load(os.path.join(snake_case__						,						'''traced_model.pt'''  )						,						map_location=snake_case__  )
                       loaded(inputs_dict['''input_ids'''].to(snake_case__  )						,						inputs_dict['''attention_mask'''].to(snake_case__  )  )




@require_torch
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased'''  )
           UpperCamelCase__    :   Tuple					=				torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]  )
           with torch.no_grad():
                 UpperCamelCase__    :   Optional[int]					=				model(snake_case__  )[0]
           UpperCamelCase__    :   Optional[int]					=				torch.Size((1, 1_1, 7_6_8)  )
           self.assertEqual(output.shape						,						snake_case__  )
           UpperCamelCase__    :   Dict					=				torch.tensor(
               [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]  )

           self.assertTrue(torch.allclose(output[:, :3, :3]						,						snake_case__						,						atol=1e-4  )  )




from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
    import PIL

# soft dependency
if is_pytesseract_available():
    import pytesseract

lowerCamelCase      =logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      return [
          int(1_0_0_0 * (box[0] / width)		),
          int(1_0_0_0 * (box[1] / height)		),
          int(1_0_0_0 * (box[2] / width)		),
          int(1_0_0_0 * (box[3] / height)		),
      ]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   List[Any]					=				to_pil_image(UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__    :   Optional[int]					=				pil_image.size
      UpperCamelCase__    :   str					=				pytesseract.image_to_data(UpperCamelCase__ ,		lang=UpperCamelCase__ ,		output_type='''dict''' ,		config=UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

      # filter empty words and corresponding coordinates
      UpperCamelCase__    :   Optional[Any]					=				[idx for idx, word in enumerate(UpperCamelCase__		) if not word.strip()]
      UpperCamelCase__    :   int					=				[word for idx, word in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   int					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   List[Any]					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   List[Any]					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   Dict					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      UpperCamelCase__    :   str					=				[]
      for x, y, w, h in zip(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
            UpperCamelCase__    :   List[Any]					=				[x, y, x + w, y + h]
            actual_boxes.append(UpperCamelCase__		)

      # finally, normalize the bounding boxes
      UpperCamelCase__    :   str					=				[]
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)		)

      assert len(UpperCamelCase__		) == len(UpperCamelCase__		), "Not as many words as there are bounding boxes"

      return words, normalized_boxes




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  ['''pixel_values''']






     def __init__(							self						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = 1 / 2_5_5						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ""						,						**__SCREAMING_SNAKE_CASE						,						)  ->   None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           UpperCamelCase__    :   Optional[int]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				do_resize
           UpperCamelCase__    :   Optional[int]					=				size
           UpperCamelCase__    :   Optional[Any]					=				resample
           UpperCamelCase__    :   Optional[Any]					=				do_rescale
           UpperCamelCase__    :   str					=				rescale_value
           UpperCamelCase__    :   Any					=				do_normalize
           UpperCamelCase__    :   List[Any]					=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCamelCase__    :   Optional[int]					=				image_std if image_std is not None else IMAGENET_STANDARD_STD
           UpperCamelCase__    :   Dict					=				apply_ocr
           UpperCamelCase__    :   List[Any]					=				ocr_lang
           UpperCamelCase__    :   str					=				tesseract_config






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           if "height" not in size or "width" not in size:
                 raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''  )
           UpperCamelCase__    :   int					=				(size['''height'''], size['''width'''])
           return resize(__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           return rescale(__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           return normalize(__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,						**__SCREAMING_SNAKE_CASE						,						)  ->   PIL.Image.Image:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__    :   List[str]					=				size if size is not None else self.size
           UpperCamelCase__    :   Dict					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				resample if resample is not None else self.resample
           UpperCamelCase__    :   int					=				do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__    :   List[str]					=				rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase__    :   str					=				do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase__    :   Union[str, Any]					=				image_mean if image_mean is not None else self.image_mean
           UpperCamelCase__    :   Tuple					=				image_std if image_std is not None else self.image_std
           UpperCamelCase__    :   Optional[Any]					=				apply_ocr if apply_ocr is not None else self.apply_ocr
           UpperCamelCase__    :   List[str]					=				ocr_lang if ocr_lang is not None else self.ocr_lang
           UpperCamelCase__    :   List[Any]					=				tesseract_config if tesseract_config is not None else self.tesseract_config

           UpperCamelCase__    :   List[Any]					=				make_list_of_images(__SCREAMING_SNAKE_CASE  )

           if not valid_images(__SCREAMING_SNAKE_CASE  ):
                 raise ValueError(
                     '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                     '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

           if do_resize and size is None:
                 raise ValueError('''Size must be specified if do_resize is True.'''  )

           if do_rescale and rescale_factor is None:
                 raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''  )

           # All transformations expect numpy arrays.
           UpperCamelCase__    :   List[Any]					=				[to_numpy_array(__SCREAMING_SNAKE_CASE  ) for image in images]

           # Tesseract OCR to get words + normalized bounding boxes
           if apply_ocr:
                 requires_backends(self						,						'''pytesseract'''  )
                 UpperCamelCase__    :   Optional[int]					=				[]
                 UpperCamelCase__    :   List[Any]					=				[]
                 for image in images:
                       UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				apply_tesseract(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                       words_batch.append(__SCREAMING_SNAKE_CASE  )
                       boxes_batch.append(__SCREAMING_SNAKE_CASE  )

           if do_resize:
                 UpperCamelCase__    :   str					=				[self.resize(image=__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_rescale:
                 UpperCamelCase__    :   List[Any]					=				[self.rescale(image=__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_normalize:
                 UpperCamelCase__    :   str					=				[self.normalize(image=__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   int					=				[to_channel_dimension_format(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   List[Any]					=				BatchFeature(data={'''pixel_values''': images}						,						tensor_type=__SCREAMING_SNAKE_CASE  )

           if apply_ocr:
                 UpperCamelCase__    :   str					=				words_batch
                 UpperCamelCase__    :   Union[str, Any]					=				boxes_batch
           return data


