
"""simple docstring"""





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_=None  ,				UpperCamelCase_=None				):
		if attention_mask is None:
				__SCREAMING_SNAKE_CASE            = tf.cast(tf.math.not_equal(UpperCamelCase_  ,				config.pad_token_id				)  ,				tf.inta				)
		return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		__lowercase							:  Union[str, Any]						   =		OPTConfig
		__lowercase							:  Union[str, Any]						   =		{}
		__lowercase							:  List[Any]						   =		'''gelu'''




		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__=1_3							,  lowerCAmelCase__=7							,  lowerCAmelCase__=True							,  lowerCAmelCase__=False							,  lowerCAmelCase__=9_9							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=2							,  lowerCAmelCase__=4							,  lowerCAmelCase__=4							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=2_0							,  lowerCAmelCase__=2							,  lowerCAmelCase__=1							,  lowerCAmelCase__=0							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=1_6							,  ):
				__SCREAMING_SNAKE_CASE            = parent
				__SCREAMING_SNAKE_CASE            = batch_size
				__SCREAMING_SNAKE_CASE            = seq_length
				__SCREAMING_SNAKE_CASE            = is_training
				__SCREAMING_SNAKE_CASE            = use_labels
				__SCREAMING_SNAKE_CASE            = vocab_size
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = intermediate_size
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = hidden_dropout_prob
				__SCREAMING_SNAKE_CASE            = attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE            = max_position_embeddings
				__SCREAMING_SNAKE_CASE            = eos_token_id
				__SCREAMING_SNAKE_CASE            = pad_token_id
				__SCREAMING_SNAKE_CASE            = bos_token_id
				__SCREAMING_SNAKE_CASE            = embed_dim
				__SCREAMING_SNAKE_CASE            = word_embed_proj_dim
				__SCREAMING_SNAKE_CASE            = False




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = ids_tensor([self.batch_size, self.seq_length - 1]							,  self.vocab_size)
				__SCREAMING_SNAKE_CASE            = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)							,  1)
				__SCREAMING_SNAKE_CASE            = tf.concat([input_ids, eos_tensor]							,  axis=1)

				__SCREAMING_SNAKE_CASE            = self.config_cls(
				    vocab_size=self.vocab_size							,  hidden_size=self.hidden_size							,  num_hidden_layers=self.num_hidden_layers							,  num_attention_heads=self.num_attention_heads							,  ffn_dim=self.intermediate_size							,  dropout=self.hidden_dropout_prob							,  attention_dropout=self.attention_probs_dropout_prob							,  max_position_embeddings=self.max_position_embeddings							,  eos_token_id=self.eos_token_id							,  bos_token_id=self.bos_token_id							,  pad_token_id=self.pad_token_id							,  embed_dim=self.embed_dim							,  word_embed_proj_dim=self.word_embed_proj_dim							,  is_encoder_decoder=lowerCAmelCase__							,  **self.config_updates							,  )
				__SCREAMING_SNAKE_CASE            = prepare_opt_inputs_dict(lowerCAmelCase__							,  lowerCAmelCase__)
				return config, inputs_dict




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = TFOPTModel(config=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = inputs_dict["""input_ids"""]

				__SCREAMING_SNAKE_CASE            = input_ids[:1, :]
				__SCREAMING_SNAKE_CASE            = inputs_dict["""attention_mask"""][:1, :]
				__SCREAMING_SNAKE_CASE            = 1

				# first forward pass
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  use_cache=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				__SCREAMING_SNAKE_CASE            = ids_tensor((self.batch_size, 3)							,  config.vocab_size)
				__SCREAMING_SNAKE_CASE            = tf.cast(ids_tensor((self.batch_size, 3)							,  2)							,  tf.inta)

				# append to next input_ids and
				__SCREAMING_SNAKE_CASE            = tf.concat([input_ids, next_tokens]							,  axis=-1)
				__SCREAMING_SNAKE_CASE            = tf.concat([attention_mask, next_attn_mask]							,  axis=-1)

				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__)[0]
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  past_key_values=lowerCAmelCase__)[0]

				self.parent.assertEqual(next_tokens.shape[1]							,  output_from_past.shape[1])

				# select random slice
				__SCREAMING_SNAKE_CASE            = int(ids_tensor((1,)							,  output_from_past.shape[-1]))
				__SCREAMING_SNAKE_CASE            = output_from_no_past[:, -3:, random_slice_idx]
				__SCREAMING_SNAKE_CASE            = output_from_past[:, :, random_slice_idx]

				# test that outputs are equal for slice
				tf.debugging.assert_near(lowerCAmelCase__							,  lowerCAmelCase__							,  rtol=1E-3)



@require_tf
class 						SCREAMING_SNAKE_CASE_ (						__a     ,				__a     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  List[str]						   =		(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
		__lowercase							:  Any						   =		(TFOPTForCausalLM,) if is_tf_available() else ()
		__lowercase							:  int						   =		(
		    {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
		)
		__lowercase							:  List[str]						   =		False
		__lowercase							:  Union[str, Any]						   =		False
		__lowercase							:  Tuple						   =		False
		__lowercase							:  Union[str, Any]						   =		10




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = TFOPTModelTester(self)
				__SCREAMING_SNAKE_CASE            = ConfigTester(self							,  config_class=lowerCAmelCase__)




		def 	snake_case_     (     self):
				self.config_tester.run_common_tests()




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs_for_common()

				def _get_word_embedding_weight(lowerCAmelCase__							,  lowerCAmelCase__):
						if hasattr(lowerCAmelCase__							,  """weight"""):
								return embedding_layer.weight
						else:
								# Here we build the word embeddings weights if not exists.
								# And then we retry to get the attribute once built.
								model.build()
								if hasattr(lowerCAmelCase__							,  """weight"""):
										return embedding_layer.weight
								else:
										return None

				for model_class in self.all_model_classes:
						for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
								# build the embeddings
								__SCREAMING_SNAKE_CASE            = model_class(config=lowerCAmelCase__)
								__SCREAMING_SNAKE_CASE            = _get_word_embedding_weight(lowerCAmelCase__							,  model.get_input_embeddings())
								__SCREAMING_SNAKE_CASE            = _get_word_embedding_weight(lowerCAmelCase__							,  model.get_output_embeddings())

								# reshape the embeddings
								model.resize_token_embeddings(lowerCAmelCase__)
								__SCREAMING_SNAKE_CASE            = _get_word_embedding_weight(lowerCAmelCase__							,  model.get_input_embeddings())
								__SCREAMING_SNAKE_CASE            = _get_word_embedding_weight(lowerCAmelCase__							,  model.get_output_embeddings())

								# check that the resized embeddings size matches the desired size.
								__SCREAMING_SNAKE_CASE            = size if size is not None else config.vocab_size

								self.assertEqual(new_input_embeddings.shape[0]							,  lowerCAmelCase__)

								# check that weights remain the same after resizing
								__SCREAMING_SNAKE_CASE            = True
								for pa, pa in zip(old_input_embeddings.value()							,  new_input_embeddings.value()):
										if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
												__SCREAMING_SNAKE_CASE            = False
								self.assertTrue(lowerCAmelCase__)

								if old_output_embeddings is not None and new_output_embeddings is not None:
										self.assertEqual(new_output_embeddings.shape[0]							,  lowerCAmelCase__)

										__SCREAMING_SNAKE_CASE            = True
										for pa, pa in zip(old_output_embeddings.value()							,  new_output_embeddings.value()):
												if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
														__SCREAMING_SNAKE_CASE            = False
										self.assertTrue(lowerCAmelCase__)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		return tf.constant(UpperCamelCase_  ,				dtype=tf.intaa				)
@require_tf
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  Union[str, Any]						   =		99




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = tf.ones((4, 1)							,  dtype=tf.intaa) * 2
				__SCREAMING_SNAKE_CASE            = tf.concat([ids_tensor((4, 6)							,  self.vocab_size - 3) + 3, eos_column_vector]							,  axis=1)
				__SCREAMING_SNAKE_CASE            = input_ids.shape[0]
				__SCREAMING_SNAKE_CASE            = OPTConfig(
				    vocab_size=self.vocab_size							,  hidden_size=2_4							,  num_hidden_layers=2							,  num_attention_heads=2							,  ffn_dim=3_2							,  max_position_embeddings=4_8							,  eos_token_id=2							,  pad_token_id=1							,  bos_token_id=0							,  )
				return config, input_ids, batch_size



@require_sentencepiece
@require_tf
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		@slow
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = TFOPTModel.from_pretrained("""facebook/opt-350m""")
				__SCREAMING_SNAKE_CASE            = _long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]])
				__SCREAMING_SNAKE_CASE            = tf.not_equal(lowerCAmelCase__							,  model.config.pad_token_id)
				with tf.GradientTape():
						__SCREAMING_SNAKE_CASE            = model(input_ids=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__).last_hidden_state
				__SCREAMING_SNAKE_CASE            = (1, 1_1, 5_1_2)
				self.assertEqual(output.shape							,  lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tf.constant(
				    [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]])
				self.assertTrue(np.allclose(output[:, :3, :3]							,  lowerCAmelCase__							,  atol=4E-3))

				__SCREAMING_SNAKE_CASE            = tf.function(lowerCAmelCase__							,  jit_compile=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = xla_generate(lowerCAmelCase__							,  lowerCAmelCase__)[0]
				self.assertTrue(np.allclose(output[:, :3, :3]							,  lowerCAmelCase__							,  atol=4E-2))



@require_tf
@slow
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		def 	snake_case_     (     self):
				super().setUp()
				__SCREAMING_SNAKE_CASE            = """facebook/opt-350m"""




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = TFOPTForCausalLM.from_pretrained(self.path_model)
				__SCREAMING_SNAKE_CASE            = GPTaTokenizer.from_pretrained(self.path_model)

				__SCREAMING_SNAKE_CASE            = [
				    """Today is a beautiful day and I want to""",
				    """In the city of""",
				    """Paris is the capital of France and""",
				    """Computers and mobile phones have taken""",
				]
				# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
				__SCREAMING_SNAKE_CASE            = tokenizer(lowerCAmelCase__							,  return_tensors="""tf"""							,  padding=lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tf.math.reduce_mean(model(inputs.input_ids							,  attention_mask=inputs.attention_mask)[0]							,  axis=-1)
				__SCREAMING_SNAKE_CASE            = tf.constant(
				    [
				        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
				        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
				        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
				        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
				    ])
				self.assertTrue(np.allclose(lowerCAmelCase__							,  lowerCAmelCase__							,  atol=1E-4))

				__SCREAMING_SNAKE_CASE            = tf.function(lowerCAmelCase__							,  jit_compile=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tf.math.reduce_mean(xla_generate(inputs.input_ids							,  attention_mask=inputs.attention_mask)[0]							,  axis=-1)
				self.assertTrue(np.allclose(lowerCAmelCase__							,  lowerCAmelCase__							,  atol=1E-4))



@require_tf
@slow
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		@property
		def 	snake_case_     (     self):
				return [
				    "Today is a beautiful day and I want",
				    "In the city of",
				    "Paris is the capital of France and",
				    "Computers and mobile phones have taken",
				]




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """facebook/opt-125m"""

				__SCREAMING_SNAKE_CASE            = [
				    """Today is a beautiful day and I want to""",
				    """In the city of New York, the city""",
				    """Paris is the capital of France and the capital""",
				    """Computers and mobile phones have taken over the""",
				]

				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = GPTaTokenizer.from_pretrained(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = TFOPTForCausalLM.from_pretrained(lowerCAmelCase__)

				for prompt in self.prompts:
						__SCREAMING_SNAKE_CASE            = tokenizer(lowerCAmelCase__							,  return_tensors="""tf""").input_ids

						__SCREAMING_SNAKE_CASE            = model.generate(lowerCAmelCase__							,  max_length=1_0)

						__SCREAMING_SNAKE_CASE            = tokenizer.batch_decode(lowerCAmelCase__							,  skip_special_tokens=lowerCAmelCase__)
						predicted_outputs += generated_string

				self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """facebook/opt-350m"""

				__SCREAMING_SNAKE_CASE            = GPTaTokenizer.from_pretrained(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = TFOPTForCausalLM.from_pretrained(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = """left"""

				# use different length sentences to test batching
				__SCREAMING_SNAKE_CASE            = [
				    """Hello, my dog is a little""",
				    """Today, I""",
				]

				__SCREAMING_SNAKE_CASE            = tokenizer(lowerCAmelCase__							,  return_tensors="""tf"""							,  padding=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = inputs["""input_ids"""]

				__SCREAMING_SNAKE_CASE            = model.generate(input_ids=lowerCAmelCase__							,  attention_mask=inputs["""attention_mask"""])

				__SCREAMING_SNAKE_CASE            = tokenizer(sentences[0]							,  return_tensors="""tf""").input_ids
				__SCREAMING_SNAKE_CASE            = model.generate(input_ids=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
				    tf.cast(inputs["""attention_mask"""][-1]							,  tf.intaa))
				__SCREAMING_SNAKE_CASE            = tokenizer(sentences[1]							,  return_tensors="""tf""").input_ids
				__SCREAMING_SNAKE_CASE            = model.generate(input_ids=lowerCAmelCase__							,  max_length=model.config.max_length - num_paddings)

				__SCREAMING_SNAKE_CASE            = tokenizer.batch_decode(lowerCAmelCase__							,  skip_special_tokens=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tokenizer.decode(output_non_padded[0]							,  skip_special_tokens=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tokenizer.decode(output_padded[0]							,  skip_special_tokens=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = [
				    """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
				    """Today, I was in the middle of a conversation with a friend about the""",
				]
				self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__)
				self.assertListEqual(lowerCAmelCase__							,  [non_padded_sentence, padded_sentence])




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """facebook/opt-350m"""

				__SCREAMING_SNAKE_CASE            = [
				    """Today is a beautiful day and I want to""",
				    """In the city of San Francisco, the city""",
				    """Paris is the capital of France and the capital""",
				    """Computers and mobile phones have taken over the""",
				]

				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = GPTaTokenizer.from_pretrained(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = TFOPTForCausalLM.from_pretrained(lowerCAmelCase__)

				for prompt in self.prompts:
						__SCREAMING_SNAKE_CASE            = tokenizer(lowerCAmelCase__							,  return_tensors="""tf""").input_ids

						__SCREAMING_SNAKE_CASE            = model.generate(lowerCAmelCase__							,  max_length=1_0)

						__SCREAMING_SNAKE_CASE            = tokenizer.batch_decode(lowerCAmelCase__							,  skip_special_tokens=lowerCAmelCase__)
						predicted_outputs += generated_string

				self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__)






"""simple docstring"""





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()
__magic_name__       =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__magic_name__       =			[]
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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = state_dict.pop(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = val



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						__SCREAMING_SNAKE_CASE            = key.replace("""backbone.0.body"""  ,				"""backbone.conv_encoder.model"""				)
						__SCREAMING_SNAKE_CASE            = value
				else:
						__SCREAMING_SNAKE_CASE            = value

		return new_state_dict



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = """"""

		# 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)
				__SCREAMING_SNAKE_CASE            = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"				)
				__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = in_proj_weight[:256, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias[:256]
				__SCREAMING_SNAKE_CASE            = in_proj_weight[256:512, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias[256:512]
				__SCREAMING_SNAKE_CASE            = in_proj_weight[-256:, :]
				__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"				)
				__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = in_proj_weight[:256, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias[:256]
				__SCREAMING_SNAKE_CASE            = in_proj_weight[256:512, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias[256:512]
				__SCREAMING_SNAKE_CASE            = in_proj_weight[-256:, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				__SCREAMING_SNAKE_CASE            = state_dict.pop(
				    f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"				)
				__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = in_proj_weight_cross_attn[:256, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias_cross_attn[:256]
				__SCREAMING_SNAKE_CASE            = in_proj_weight_cross_attn[256:512, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias_cross_attn[256:512]
				__SCREAMING_SNAKE_CASE            = in_proj_weight_cross_attn[-256:, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias_cross_attn[-256:]



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = image.size
		__SCREAMING_SNAKE_CASE            = max(UpperCamelCase_  ,				UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = 800 if """detection""" in checkpoint_url else 1000
		__SCREAMING_SNAKE_CASE            = target_max_size / current_max_size
		__SCREAMING_SNAKE_CASE            = image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

		return resized_image



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = F.to_tensor(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = F.normalize(UpperCamelCase_  ,				mean=[0.485, 0.456, 0.406]  ,				std=[0.229, 0.224, 0.225]				)
		return image



@torch.no_grad()
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		logger.info("""Converting model..."""				)

		# load original state dict
		__SCREAMING_SNAKE_CASE            = torch.hub.load_state_dict_from_url(UpperCamelCase_  ,				map_location="""cpu"""				)
		# rename keys
		for src, dest in rename_keys:
				rename_key(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = rename_backbone_keys(UpperCamelCase_				)
		# query, key and value matrices need special treatment
		read_in_q_k_v(UpperCamelCase_				)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		__SCREAMING_SNAKE_CASE            = """model."""
		for key in state_dict.copy().keys():
				if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
						__SCREAMING_SNAKE_CASE            = state_dict.pop(UpperCamelCase_				)
						__SCREAMING_SNAKE_CASE            = val
    # create HuggingFace model and load state dict
		__SCREAMING_SNAKE_CASE            = 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:
				__SCREAMING_SNAKE_CASE            = 15
				__SCREAMING_SNAKE_CASE            = 2
				__SCREAMING_SNAKE_CASE            = {0: """table""", 1: """table rotated"""}
				__SCREAMING_SNAKE_CASE            = idalabel
				__SCREAMING_SNAKE_CASE            = {v: k for k, v in idalabel.items()}
		else:
				__SCREAMING_SNAKE_CASE            = 125
				__SCREAMING_SNAKE_CASE            = 6
				__SCREAMING_SNAKE_CASE            = {
				    0: """table""",
				    1: """table column""",
				    2: """table row""",
				    3: """table column header""",
				    4: """table projected row header""",
				    5: """table spanning cell""",
				}
				__SCREAMING_SNAKE_CASE            = idalabel
				__SCREAMING_SNAKE_CASE            = {v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE            = DetrImageProcessor(
		    format="""coco_detection"""  ,				max_size=800 if """detection""" in checkpoint_url else 1000				)
		__SCREAMING_SNAKE_CASE            = TableTransformerForObjectDetection(UpperCamelCase_				)
		model.load_state_dict(UpperCamelCase_				)
		model.eval()

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

		__SCREAMING_SNAKE_CASE            = model(UpperCamelCase_				)

		if "detection" in checkpoint_url:
				__SCREAMING_SNAKE_CASE            = (1, 15, 3)
				__SCREAMING_SNAKE_CASE            = torch.tensor(
				    [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]				)
				__SCREAMING_SNAKE_CASE            = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]				)

		else:
				__SCREAMING_SNAKE_CASE            = (1, 125, 7)
				__SCREAMING_SNAKE_CASE            = torch.tensor(
				    [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]				)
				__SCREAMING_SNAKE_CASE            = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]				)

		assert outputs.logits.shape == expected_shape
		assert torch.allclose(outputs.logits[0, :3, :3]  ,				UpperCamelCase_  ,				atol=1e-4				)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,				UpperCamelCase_  ,				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(UpperCamelCase_				).mkdir(exist_ok=UpperCamelCase_				)
				model.save_pretrained(UpperCamelCase_				)
				image_processor.save_pretrained(UpperCamelCase_				)

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


if __name__ == "__main__":
							__magic_name__       =			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."
							)
							__magic_name__       =			parser.parse_args()
							convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




