'''simple docstring'''




import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase          =  logging.get_logger(__name__)






def 				snake_case__							(			_A:					str      ,   _A:					Any )    ->	Optional[int]:

		'''simple docstring'''


		lowerCAmelCase   					=  []
		for i in range(encoder_config.num_hidden_layers ):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append(
				    (f"encoder.deit.blocks.{i}.norm1.weight", f"encoder.encoder.layer.{i}.layernorm_before.weight") )
				rename_keys.append((f"encoder.deit.blocks.{i}.norm1.bias", f"encoder.encoder.layer.{i}.layernorm_before.bias") )
				rename_keys.append(
				    (f"encoder.deit.blocks.{i}.attn.proj.weight", f"encoder.encoder.layer.{i}.attention.output.dense.weight") )
				rename_keys.append(
				    (f"encoder.deit.blocks.{i}.attn.proj.bias", f"encoder.encoder.layer.{i}.attention.output.dense.bias") )
				rename_keys.append(
				    (f"encoder.deit.blocks.{i}.norm2.weight", f"encoder.encoder.layer.{i}.layernorm_after.weight") )
				rename_keys.append((f"encoder.deit.blocks.{i}.norm2.bias", f"encoder.encoder.layer.{i}.layernorm_after.bias") )
				rename_keys.append(
				    (f"encoder.deit.blocks.{i}.mlp.fc1.weight", f"encoder.encoder.layer.{i}.intermediate.dense.weight") )
				rename_keys.append(
				    (f"encoder.deit.blocks.{i}.mlp.fc1.bias", f"encoder.encoder.layer.{i}.intermediate.dense.bias") )
				rename_keys.append(
				    (f"encoder.deit.blocks.{i}.mlp.fc2.weight", f"encoder.encoder.layer.{i}.output.dense.weight") )
				rename_keys.append((f"encoder.deit.blocks.{i}.mlp.fc2.bias", f"encoder.encoder.layer.{i}.output.dense.bias") )

		# cls token, position embeddings and patch embeddings of encoder
		rename_keys.extend(
		    [
		        ("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
		        ("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
		        ("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
		        ("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
		        ("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
		        ("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
		    ] )

		return rename_keys






def 				snake_case__							(			_A:					Dict      ,   _A:					List[Any] )    ->	Any:

		'''simple docstring'''


		for i in range(encoder_config.num_hidden_layers ):
				# queries, keys and values (only weights, no biases)
				lowerCAmelCase   					=  state_dict.pop(f"encoder.deit.blocks.{i}.attn.qkv.weight" )

				lowerCAmelCase   					=  in_proj_weight[
				    : encoder_config.hidden_size, :
				]
				lowerCAmelCase   					=  in_proj_weight[
				    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
				]
				lowerCAmelCase   					=  in_proj_weight[
				    -encoder_config.hidden_size :, :
				]






def 				snake_case__							(			_A:					Optional[Any]      ,   _A:					int      ,   _A:					int )    ->	Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  dct.pop(snake_case_ )
		lowerCAmelCase   					=  val






def 				snake_case__							(			_A:					List[Any] )    ->	Tuple:

		'''simple docstring'''


		if "handwritten" in checkpoint_url:
				lowerCAmelCase   					=  """https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"""  # industry
				# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
				# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
				# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
				# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
		elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
				lowerCAmelCase   					=  """https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
		lowerCAmelCase   					=  Image.open(requests.get(snake_case_      ,   stream=snake_case_ ).raw ).convert("""RGB""" )
		return im






@torch.no_grad()
def 				snake_case__							(			_A:					List[Any]      ,   _A:					Any )    ->	Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  ViTConfig(image_size=384      ,   qkv_bias=snake_case_ )
		lowerCAmelCase   					=  TrOCRConfig()

		# size of the architecture
		if "base" in checkpoint_url:
				lowerCAmelCase   					=  768
		elif "large" in checkpoint_url:
				# use ViT-large encoder
				lowerCAmelCase   					=  1024
				lowerCAmelCase   					=  4096
				lowerCAmelCase   					=  24
				lowerCAmelCase   					=  16
				lowerCAmelCase   					=  1024
		else:
				raise ValueError("""Should either find 'base' or 'large' in checkpoint URL""" )

		# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
		if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
				lowerCAmelCase   					=  False
				lowerCAmelCase   					=  """relu"""
				lowerCAmelCase   					=  1024
				lowerCAmelCase   					=  True
				lowerCAmelCase   					=  False
				lowerCAmelCase   					=  False

		# load HuggingFace model
		lowerCAmelCase   					=  ViTModel(snake_case_      ,   add_pooling_layer=snake_case_ )
		lowerCAmelCase   					=  TrOCRForCausalLM(snake_case_ )
		lowerCAmelCase   					=  VisionEncoderDecoderModel(encoder=snake_case_      ,   decoder=snake_case_ )
		model.eval()

		# load state_dict of original model, rename some keys
		lowerCAmelCase   					=  torch.hub.load_state_dict_from_url(snake_case_      ,   map_location="""cpu"""      ,   check_hash=snake_case_ )["""model"""]

		lowerCAmelCase   					=  create_rename_keys(snake_case_      ,   snake_case_ )
		for src, dest in rename_keys:
				rename_key(snake_case_      ,   snake_case_      ,   snake_case_ )
		read_in_q_k_v(snake_case_      ,   snake_case_ )

		# remove parameters we don't need
		del state_dict["encoder.deit.head.weight"]
		del state_dict["encoder.deit.head.bias"]
		del state_dict["decoder.version"]

		# add prefix to decoder keys
		for key, val in state_dict.copy().items():
				lowerCAmelCase   					=  state_dict.pop(snake_case_ )
				if key.startswith("""decoder""" ) and "output_projection" not in key:
						lowerCAmelCase   					=  val
				else:
						lowerCAmelCase   					=  val

    # load state dict
		model.load_state_dict(snake_case_ )

		# Check outputs on an image
		lowerCAmelCase   					=  ViTImageProcessor(size=encoder_config.image_size )
		lowerCAmelCase   					=  RobertaTokenizer.from_pretrained("""roberta-large""" )
		lowerCAmelCase   					=  TrOCRProcessor(snake_case_      ,   snake_case_ )

		lowerCAmelCase   					=  processor(images=prepare_img(snake_case_ )      ,   return_tensors="""pt""" ).pixel_values

		# verify logits
		lowerCAmelCase   					=  torch.tensor([[model.config.decoder.decoder_start_token_id]] )
		lowerCAmelCase   					=  model(pixel_values=snake_case_      ,   decoder_input_ids=snake_case_ )
		lowerCAmelCase   					=  outputs.logits

		lowerCAmelCase   					=  torch.Size([1, 1, 50265] )
		if "trocr-base-handwritten" in checkpoint_url:
				lowerCAmelCase   					=  torch.tensor(
				    [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311] )
		elif "trocr-large-handwritten" in checkpoint_url:
				lowerCAmelCase   					=  torch.tensor(
				    [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170] )
		elif "trocr-base-printed" in checkpoint_url:
				lowerCAmelCase   					=  torch.tensor(
				    [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210] )
		elif "trocr-large-printed" in checkpoint_url:
				lowerCAmelCase   					=  torch.tensor(
				    [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535] )

		if "stage1" not in checkpoint_url:
				assert logits.shape == expected_shape, "Shape of logits not as expected"
				assert torch.allclose(logits[0, 0, :10]      ,   snake_case_      ,   atol=1e-3 ), "First elements of logits not as expected"

		Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
		print(f"Saving model to {pytorch_dump_folder_path}" )
		model.save_pretrained(snake_case_ )
		print(f"Saving processor to {pytorch_dump_folder_path}" )
		processor.save_pretrained(snake_case_ )


if __name__ == "__main__":
						__lowercase          =  argparse.ArgumentParser()

						parser.add_argument(
						    '''--checkpoint_url''',
						    default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
						    type=str,
						    help='''URL to the original PyTorch checkpoint (.pth file).''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
						)
						__lowercase          =  parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
  import torch







@is_pipeline_test
class 						lowercase							(   unittest.TestCase		):
     _a							 =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     _a							 =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							int:
           _A							:     str			=						TextaTextGenerationPipeline(model=_a		,     tokenizer=_a				)
           return generator, ["Something to write", "Something else"]

     def 					a__	(  self		,     _a		,     _a				)						->							Dict:
           _A							:     Any			=						generator("""Something there"""				)
           self.assertEqual(_a		,     [{"""generated_text""": ANY(_a				)}]				)
           # These are encoder decoder, they don't just append to incoming string
           self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""				)				)

           _A							:     List[Any]			=						generator(["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           _A							:     Optional[int]			=						generator(
               ["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     batch_size=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           with self.assertRaises(_a				):
                 generator(4				)

     @require_torch
     def 					a__	(  self				)						->							List[str]:
           _A							:     Any			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""pt"""				)
           # do_sample=False necessary for reproducibility
           _A							:     Dict			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

           _A							:     Any			=						3
           _A							:     Any			=						generator(
               """Something there"""		,     num_return_sequences=_a		,     num_beams=_a		,     )
           _A							:     Optional[int]			=						[
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """"""},
           ]
           self.assertEqual(_a		,     _a				)

           _A							:     Dict			=						generator("""This is a test"""		,     do_sample=_a		,     num_return_sequences=2		,     return_tensors=_a				)
           self.assertEqual(
               _a		,     [
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
               ]		,     )
           _A							:     Dict			=						generator.model.config.eos_token_id
           _A							:     List[str]			=						"""<pad>"""
           _A							:     Dict			=						generator(
               ["""This is a test""", """This is a second test"""]		,     do_sample=_a		,     num_return_sequences=2		,     batch_size=2		,     return_tensors=_a		,     )
           self.assertEqual(
               _a		,     [
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
               ]		,     )



     @require_tf
     def 					a__	(  self				)						->							int:
           _A							:     Optional[Any]			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""tf"""				)
           # do_sample=False necessary for reproducibility
           _A							:     str			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

