




import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   MODEL_FOR_CAUSAL_LM_MAPPING
    UpperCAmelCase__													=   TF_MODEL_FOR_CAUSAL_LM_MAPPING


    @require_torch
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					pipeline(task='''text-generation'''      ,				model='''sshleifer/tiny-ctrl'''      ,				framework='''pt''')
           # Using `do_sample=False` to force deterministic output
           A__   				=					text_generator('''This is a test'''      ,				do_sample=UpperCamelCase__)
           self.assertEqual(
               UpperCamelCase__      ,				[
                   {
                       '''generated_text''': (
                           '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                           ''' oscope. FiliFili@@'''
                       )
                   }
               ]      ,				)

           A__   				=					text_generator(['''This is a test''', '''This is a second test'''])
           self.assertEqual(
               UpperCamelCase__      ,				[
                   [
                       {
                           '''generated_text''': (
                               '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                               ''' oscope. FiliFili@@'''
                           )
                       }
                   ],
                   [
                       {
                           '''generated_text''': (
                               '''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
                               ''' oscope. oscope. FiliFili@@'''
                           )
                       }
                   ],
               ]      ,				)

           A__   				=					text_generator('''This is a test'''      ,				do_sample=UpperCamelCase__      ,				num_return_sequences=2      ,				return_tensors=UpperCamelCase__)
           self.assertEqual(
               UpperCamelCase__      ,				[
                   {'''generated_token_ids''': ANY(UpperCamelCase__)},
                   {'''generated_token_ids''': ANY(UpperCamelCase__)},
               ]      ,				)
           A__   				=					text_generator.model.config.eos_token_id
           A__   				=					'<pad>'
           A__   				=					text_generator(
               ['''This is a test''', '''This is a second test''']      ,				do_sample=UpperCamelCase__      ,				num_return_sequences=2      ,				batch_size=2      ,				return_tensors=UpperCamelCase__      ,				)
           self.assertEqual(
               UpperCamelCase__      ,				[
                   [
                       {'''generated_token_ids''': ANY(UpperCamelCase__)},
                       {'''generated_token_ids''': ANY(UpperCamelCase__)},
                   ],
                   [
                       {'''generated_token_ids''': ANY(UpperCamelCase__)},
                       {'''generated_token_ids''': ANY(UpperCamelCase__)},
                   ],
               ]      ,				)


    @require_tf
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					pipeline(task='''text-generation'''      ,				model='''sshleifer/tiny-ctrl'''      ,				framework='''tf''')

           # Using `do_sample=False` to force deterministic output
           A__   				=					text_generator('''This is a test'''      ,				do_sample=UpperCamelCase__)
           self.assertEqual(
               UpperCamelCase__      ,				[
                   {
                       '''generated_text''': (
                           '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                           ''' please,'''
                       )
                   }
               ]      ,				)

           A__   				=					text_generator(['''This is a test''', '''This is a second test''']      ,				do_sample=UpperCamelCase__)
           self.assertEqual(
               UpperCamelCase__      ,				[
                   [
                       {
                           '''generated_text''': (
                               '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                               ''' please,'''
                           )
                       }
                   ],
                   [
                       {
                           '''generated_text''': (
                               '''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
                               ''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
                           )
                       }
                   ],
               ]      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Any)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					TextGenerationPipeline(model=UpperCamelCase__      ,				tokenizer=UpperCamelCase__)
           return text_generator, ["This is a test", "Another test"]


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





           '''simple docstring'''

           A__   				=					'Hello I believe in'
           A__   				=					pipeline('''text-generation'''      ,				model='''hf-internal-testing/tiny-random-gpt2''')
           A__   				=					text_generator(UpperCamelCase__)
           self.assertEqual(
               UpperCamelCase__      ,				[{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}]      ,				)

           A__   				=					text_generator(UpperCamelCase__      ,				stop_sequence=''' fe''')
           self.assertEqual(UpperCamelCase__      ,				[{'''generated_text''': '''Hello I believe in fe'''}])


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Any)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					text_generator.model
           A__   				=					text_generator.tokenizer

           A__   				=					text_generator('''This is a test''')
           self.assertEqual(UpperCamelCase__      ,				[{'''generated_text''': ANY(UpperCamelCase__)}])
           self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''))

           A__   				=					text_generator('''This is a test'''      ,				return_full_text=UpperCamelCase__)
           self.assertEqual(UpperCamelCase__      ,				[{'''generated_text''': ANY(UpperCamelCase__)}])
           self.assertNotIn('''This is a test'''      ,				outputs[0]['''generated_text'''])

           A__   				=					pipeline(task='''text-generation'''      ,				model=UpperCamelCase__      ,				tokenizer=UpperCamelCase__      ,				return_full_text=UpperCamelCase__)
           A__   				=					text_generator('''This is a test''')
           self.assertEqual(UpperCamelCase__      ,				[{'''generated_text''': ANY(UpperCamelCase__)}])
           self.assertNotIn('''This is a test'''      ,				outputs[0]['''generated_text'''])

           A__   				=					text_generator('''This is a test'''      ,				return_full_text=UpperCamelCase__)
           self.assertEqual(UpperCamelCase__      ,				[{'''generated_text''': ANY(UpperCamelCase__)}])
           self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''))

           A__   				=					text_generator(['''This is great !''', '''Something else''']      ,				num_return_sequences=2      ,				do_sample=UpperCamelCase__)
           self.assertEqual(
               UpperCamelCase__      ,				[
                   [{'''generated_text''': ANY(UpperCamelCase__)}, {'''generated_text''': ANY(UpperCamelCase__)}],
                   [{'''generated_text''': ANY(UpperCamelCase__)}, {'''generated_text''': ANY(UpperCamelCase__)}],
               ]      ,				)

           if text_generator.tokenizer.pad_token is not None:
                  A__   				=					text_generator(
                      ['''This is great !''', '''Something else''']      ,				num_return_sequences=2      ,				batch_size=2      ,				do_sample=UpperCamelCase__)
                  self.assertEqual(
                      UpperCamelCase__      ,				[
                          [{'''generated_text''': ANY(UpperCamelCase__)}, {'''generated_text''': ANY(UpperCamelCase__)}],
                          [{'''generated_text''': ANY(UpperCamelCase__)}, {'''generated_text''': ANY(UpperCamelCase__)}],
                      ]      ,				)

           with self.assertRaises(UpperCamelCase__):
                  A__   				=					text_generator('''test'''      ,				return_full_text=UpperCamelCase__      ,				return_text=UpperCamelCase__)
           with self.assertRaises(UpperCamelCase__):
                  A__   				=					text_generator('''test'''      ,				return_full_text=UpperCamelCase__      ,				return_tensors=UpperCamelCase__)
           with self.assertRaises(UpperCamelCase__):
                  A__   				=					text_generator('''test'''      ,				return_text=UpperCamelCase__      ,				return_tensors=UpperCamelCase__)

           # Empty prompt is slighly special
           # it requires BOS token to exist.
           # Special case for Pegasus which will always append EOS so will
           # work even without BOS.
           if (
               text_generator.tokenizer.bos_token_id is not None
               or "Pegasus" in tokenizer.__class__.__name__
               or "Git" in model.__class__.__name__
           ):
                  A__   				=					text_generator('''''')
                  self.assertEqual(UpperCamelCase__      ,				[{'''generated_text''': ANY(UpperCamelCase__)}])
           else:
                  with self.assertRaises((ValueError, AssertionError)):
                         A__   				=					text_generator('''''')

           if text_generator.framework == "tf":
                  # TF generation does not support max_new_tokens, and it's impossible
                  # to control long generation with only max_length without
                  # fancy calculation, dismissing tests for now.
                  return
           # We don't care about infinite range models.
           # They already work.
           # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
           A__   				=					['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
           if (
               tokenizer.model_max_length < 10_000
               and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
           ):
                  # Handling of large generations
                  with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)):
                         text_generator('''This is a test''' * 500      ,				max_new_tokens=20)

                  A__   				=					text_generator('''This is a test''' * 500      ,				handle_long_generation='''hole'''      ,				max_new_tokens=20)
                  # Hole strategy cannot work
                  with self.assertRaises(UpperCamelCase__):
                         text_generator(
                             '''This is a test''' * 500      ,				handle_long_generation='''hole'''      ,				max_new_tokens=tokenizer.model_max_length + 10      ,				)


    @require_torch
    @require_accelerate
    @require_torch_gpu
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->int:





           '''simple docstring'''

           import torch

           # Classic `model_kwargs`
           A__   				=					pipeline(
               model='''hf-internal-testing/tiny-random-bloom'''      ,				model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa}      ,				)
           self.assertEqual(pipe.model.device      ,				torch.device(0))
           self.assertEqual(pipe.model.lm_head.weight.dtype      ,				torch.bfloataa)
           A__   				=					pipe('''This is a test''')
           self.assertEqual(
               UpperCamelCase__      ,				[
                   {
                       '''generated_text''': (
                           '''This is a test test test test test test test test test test test test test test test test'''
                           ''' test'''
                       )
                   }
               ]      ,				)

           # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
           A__   				=					pipeline(model='''hf-internal-testing/tiny-random-bloom'''      ,				device_map='''auto'''      ,				torch_dtype=torch.bfloataa)
           self.assertEqual(pipe.model.device      ,				torch.device(0))
           self.assertEqual(pipe.model.lm_head.weight.dtype      ,				torch.bfloataa)
           A__   				=					pipe('''This is a test''')
           self.assertEqual(
               UpperCamelCase__      ,				[
                   {
                       '''generated_text''': (
                           '''This is a test test test test test test test test test test test test test test test test'''
                           ''' test'''
                       )
                   }
               ]      ,				)

           # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
           A__   				=					pipeline(model='''hf-internal-testing/tiny-random-bloom'''      ,				device_map='''auto''')
           self.assertEqual(pipe.model.device      ,				torch.device(0))
           self.assertEqual(pipe.model.lm_head.weight.dtype      ,				torch.floataa)
           A__   				=					pipe('''This is a test''')
           self.assertEqual(
               UpperCamelCase__      ,				[
                   {
                       '''generated_text''': (
                           '''This is a test test test test test test test test test test test test test test test test'''
                           ''' test'''
                       )
                   }
               ]      ,				)


    @require_torch
    @require_torch_gpu
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Union[str, Any]:





           '''simple docstring'''

           import torch

           A__   				=					pipeline(model='''hf-internal-testing/tiny-random-bloom'''      ,				device=0      ,				torch_dtype=torch.floataa)
           pipe('''This is a test''')


    @require_torch
    @require_accelerate
    @require_torch_gpu
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->List[Any]:





           '''simple docstring'''

           import torch

           A__   				=					pipeline(model='''hf-internal-testing/tiny-random-bloom'''      ,				device_map='''auto'''      ,				torch_dtype=torch.floataa)
           pipe('''This is a test'''      ,				do_sample=UpperCamelCase__      ,				top_p=0.5)





    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Optional[int]:





           '''simple docstring'''

           A__   				=					'Hello world'
           A__   				=					pipeline('''text-generation'''      ,				model='''hf-internal-testing/tiny-random-gpt2''')
           if text_generator.model.framework == "tf":
                  A__   				=					logging.get_logger('''transformers.generation.tf_utils''')
           else:
                  A__   				=					logging.get_logger('''transformers.generation.utils''')
           A__   				=					'Both `max_new_tokens`'  # The beggining of the message to be checked in this test

           # Both are set by the user -> log warning
           with CaptureLogger(UpperCamelCase__) as cl:
                  A__   				=					text_generator(UpperCamelCase__      ,				max_length=10      ,				max_new_tokens=1)
           self.assertIn(UpperCamelCase__      ,				cl.out)

           # The user only sets one -> no warning
           with CaptureLogger(UpperCamelCase__) as cl:
                  A__   				=					text_generator(UpperCamelCase__      ,				max_new_tokens=1)
           self.assertNotIn(UpperCamelCase__      ,				cl.out)

           with CaptureLogger(UpperCamelCase__) as cl:
                  A__   				=					text_generator(UpperCamelCase__      ,				max_length=10)
           self.assertNotIn(UpperCamelCase__      ,				cl.out)








import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase			:	str							 =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase			:	int							 =     []
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
        ("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[Any]:



       """simple docstring"""

       A__   				=					state_dict.pop(lowercase_		)
       A__   				=					val







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     A__   				=					key.replace('''backbone.0.body'''				,       '''backbone.conv_encoder.model'''		)
                     A__   				=					value
              else:
                     A__   				=					value

       return new_state_dict







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Optional[Any]:



       """simple docstring"""

       A__   				=					''''''

       # first: transformer encoder
       for i in range(6		):
              # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
              A__   				=					state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""		)
              A__   				=					state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""		)
              # next, add query, keys and values (in that order) to the state dict
              A__   				=					in_proj_weight[:256, :]
              A__   				=					in_proj_bias[:256]
              A__   				=					in_proj_weight[256:512, :]
              A__   				=					in_proj_bias[256:512]
              A__   				=					in_proj_weight[-256:, :]
              A__   				=					in_proj_bias[-256:]
       # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
       for i in range(6		):
              # read in weights + bias of input projection layer of self-attention
              A__   				=					state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""		)
              A__   				=					state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""		)
              # next, add query, keys and values (in that order) to the state dict
              A__   				=					in_proj_weight[:256, :]
              A__   				=					in_proj_bias[:256]
              A__   				=					in_proj_weight[256:512, :]
              A__   				=					in_proj_bias[256:512]
              A__   				=					in_proj_weight[-256:, :]
              A__   				=					in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              A__   				=					state_dict.pop(
                  f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""		)
              A__   				=					state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""		)
              # next, add query, keys and values (in that order) of cross-attention to the state dict
              A__   				=					in_proj_weight_cross_attn[:256, :]
              A__   				=					in_proj_bias_cross_attn[:256]
              A__   				=					in_proj_weight_cross_attn[256:512, :]
              A__   				=					in_proj_bias_cross_attn[256:512]
              A__   				=					in_proj_weight_cross_attn[-256:, :]
              A__   				=					in_proj_bias_cross_attn[-256:]







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Dict:



       """simple docstring"""

       A__				,							A__   				=					image.size
       A__   				=					max(lowercase_				,       lowercase_		)
       A__   				=					800 if '''detection''' in checkpoint_url else 1_000
       A__   				=					target_max_size / current_max_size
       A__   				=					image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

       return resized_image







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Tuple:



       """simple docstring"""

       A__   				=					F.to_tensor(lowercase_		)
       A__   				=					F.normalize(lowercase_				,       mean=[0.4_85, 0.4_56, 0.4_06]				,       std=[0.2_29, 0.2_24, 0.2_25]		)
       return image







@torch.no_grad()
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       logger.info('''Converting model...'''		)

       # load original state dict
       A__   				=					torch.hub.load_state_dict_from_url(lowercase_				,       map_location='''cpu'''		)
       # rename keys
       for src, dest in rename_keys:
              rename_key(lowercase_				,       lowercase_				,       lowercase_		)
       A__   				=					rename_backbone_keys(lowercase_		)
       # query, key and value matrices need special treatment
       read_in_q_k_v(lowercase_		)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       A__   				=					'''model.'''
       for key in state_dict.copy().keys():
              if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
                     A__   				=					state_dict.pop(lowercase_		)
                     A__   				=					val
    # create HuggingFace model and load state dict
       A__   				=					TableTransformerConfig(
           backbone='''resnet18'''				,       mask_loss_coefficient=1				,       dice_loss_coefficient=1				,       ce_loss_coefficient=1				,       bbox_loss_coefficient=5				,       giou_loss_coefficient=2				,       eos_coefficient=0.4				,       class_cost=1				,       bbox_cost=5				,       giou_cost=2				,       )

       if "detection" in checkpoint_url:
              A__   				=					15
              A__   				=					2
              A__   				=					{0: '''table''', 1: '''table rotated'''}
              A__   				=					idalabel
              A__   				=					{v: k for k, v in idalabel.items()}
       else:
              A__   				=					125
              A__   				=					6
              A__   				=					{
                  0: '''table''',
                  1: '''table column''',
                  2: '''table row''',
                  3: '''table column header''',
                  4: '''table projected row header''',
                  5: '''table spanning cell''',
              }
              A__   				=					idalabel
              A__   				=					{v: k for k, v in idalabel.items()}

       A__   				=					DetrImageProcessor(
           format='''coco_detection'''				,       max_size=800 if '''detection''' in checkpoint_url else 1_000		)
       A__   				=					TableTransformerForObjectDetection(lowercase_		)
       model.load_state_dict(lowercase_		)
       model.eval()

       # verify our conversion
       A__   				=					'''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
       A__   				=					hf_hub_download(repo_id='''nielsr/example-pdf'''				,       repo_type='''dataset'''				,       filename=lowercase_		)
       A__   				=					Image.open(lowercase_		).convert('''RGB'''		)
       A__   				=					normalize(resize(lowercase_				,       lowercase_		)		).unsqueeze(0		)

       A__   				=					model(lowercase_		)

       if "detection" in checkpoint_url:
              A__   				=					(1, 15, 3)
              A__   				=					torch.tensor(
                  [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]		)
              A__   				=					torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]		)

       else:
              A__   				=					(1, 125, 7)
              A__   				=					torch.tensor(
                  [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]		)
              A__   				=					torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]		)

       assert outputs.logits.shape == expected_shape
       assert torch.allclose(outputs.logits[0, :3, :3]				,       lowercase_				,       atol=1E-4		)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,       lowercase_				,       atol=1E-4		)
       print('''Looks ok!'''		)

       if pytorch_dump_folder_path is not None:
              # Save model and image processor
              logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""		)
              Path(lowercase_		).mkdir(exist_ok=lowercase_		)
              model.save_pretrained(lowercase_		)
              image_processor.save_pretrained(lowercase_		)

       if push_to_hub:
              # Push model to HF hub
              logger.info('''Pushing model to the hub...'''		)
              A__   				=					(
                  '''microsoft/table-transformer-detection'''
                  if '''detection''' in checkpoint_url
                  else '''microsoft/table-transformer-structure-recognition'''
              )
              model.push_to_hub(lowercase_		)
              image_processor.push_to_hub(lowercase_		)


if __name__ == "__main__":
  _lowerCamelCase			:	str							 =     argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_url""",
      default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
      type=str,
      choices=[
          """https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
          """https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
      ],
      help="""URL of the Table Transformer checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )
  _lowerCamelCase			:	str							 =     parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


