
import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						UpperCAmelCase	(		A_ ,unittest.TestCase				):
			A__      :	Dict		  =  PreTrainedTokenizerFast
			A__      :	str		  =  False
			A__      :	List[Any]		  =  True
			A__      :	List[str]		  =  "tokenizer_file"



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


										'''simple docstring'''


										snake_case :     Union[str, Any]           =   False  # because we don't have pretrained_vocab_files_map
										super().setUp()
										snake_case :     Optional[int]           =   True

										snake_case :     List[str]           =   ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
										snake_case :     Optional[Any]           =   "SaulLu/dummy-tokenizer-bytelevel-bpe"

										# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
										snake_case :     int           =   [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

										snake_case :     List[Any]           =   PreTrainedTokenizerFast.from_pretrained(model_paths[0]				)
										tokenizer.save_pretrained(self.tmpdirname				)



			def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			int:


										'''simple docstring'''


										pass



			def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			int:


										'''simple docstring'''


										pass



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


										'''simple docstring'''


										pass



			def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			List[str]:


										'''simple docstring'''


										pass



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


										'''simple docstring'''


										snake_case :     Optional[int]           =   self.tmpdirname
										# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																								try:
																															snake_case :     Any           =   tempfile.mkdtemp()
																															snake_case :     int           =   self.rust_tokenizer_class.from_pretrained(snake_case__					,		**snake_case__				)

																															tokenizer.save_pretrained(self.tmpdirname				)
																															super().test_training_new_tokenizer()
																								finally:
																															# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																															# is restored
																															shutil.rmtree(self.tmpdirname				)
																															snake_case :     int           =   tmpdirname_orig



			def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Dict:


										'''simple docstring'''


										snake_case :     Optional[int]           =   self.tmpdirname
										# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																								try:
																															snake_case :     Union[str, Any]           =   tempfile.mkdtemp()
																															snake_case :     str           =   self.rust_tokenizer_class.from_pretrained(snake_case__					,		**snake_case__				)

																															tokenizer.save_pretrained(self.tmpdirname				)
																															super().test_training_new_tokenizer_with_special_tokens_change()
																								finally:
																															# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																															# is restored
																															shutil.rmtree(self.tmpdirname				)
																															snake_case :     Dict           =   tmpdirname_orig



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


										'''simple docstring'''


										snake_case :     int           =   self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name				)

										snake_case :     int           =   ("a" for _ in range(10_00				))
										snake_case :     Optional[int]           =   tokenizer.train_new_from_iterator(text_iterator=snake_case__					,		length=10_00					,		vocab_size=50				)

										snake_case :     str           =   new_tokenizer.encode("a🤗"				)
										self.assertEqual(snake_case__					,		[64, 1_72, 2_53, 97, 2_45]				)






@require_tokenizers
class 						UpperCAmelCase	(		unittest.TestCase				):



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


										'''simple docstring'''


										snake_case :     List[str]           =   AutoTokenizer.from_pretrained("bert-base-cased"				)
										snake_case :     Union[str, Any]           =   json.loads(tokenizer._tokenizer.to_str()				)
										snake_case :     Tuple           =   len(snake_case__				)

										with tempfile.TemporaryDirectory() as tmp_dir:
																	# Hack to save this in the tokenizer_config.json
																	snake_case :     List[str]           =   ["tokenizer.4.0.0.json"]
																	tokenizer.save_pretrained(snake_case__				)
																	json.dump(snake_case__					,		open(os.path.join(snake_case__					,		"tokenizer.4.0.0.json"				)					,		"w"				)				)

																	# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
																	snake_case :     Optional[Any]           =   AutoTokenizer.from_pretrained(snake_case__				)
																	self.assertEqual(len(snake_case__				)					,		len(snake_case__				) + 1				)
																	snake_case :     Union[str, Any]           =   json.loads(new_tokenizer._tokenizer.to_str()				)
																	self.assertIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)

																	# Will need to be adjusted if we reach v42 and this test is still here.
																	# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
																	shutil.move(os.path.join(snake_case__					,		"tokenizer.4.0.0.json"				)					,		os.path.join(snake_case__					,		"tokenizer.42.0.0.json"				)				)
																	snake_case :     Optional[Any]           =   ["tokenizer.42.0.0.json"]
																	tokenizer.save_pretrained(snake_case__				)
																	snake_case :     Dict           =   AutoTokenizer.from_pretrained(snake_case__				)
																	self.assertEqual(len(snake_case__				)					,		len(snake_case__				)				)
																	snake_case :     Optional[int]           =   json.loads(new_tokenizer._tokenizer.to_str()				)
																	self.assertNotIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)



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


										'''simple docstring'''


										snake_case :     int           =   "hf-internal-testing/test-two-tokenizers"

										# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
										snake_case :     int           =   AutoTokenizer.from_pretrained(snake_case__				)
										self.assertEqual(len(snake_case__				)					,		2_89_97				)
										snake_case :     Optional[int]           =   json.loads(tokenizer._tokenizer.to_str()				)
										self.assertIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)

										# Testing an older version by monkey-patching the version in the module it's used.
										import transformers as old_transformers

										snake_case :     Dict           =   "3.0.0"
										snake_case :     Union[str, Any]           =   old_transformers.models.auto.AutoTokenizer.from_pretrained(snake_case__				)
										self.assertEqual(len(snake_case__				)					,		2_89_96				)
										snake_case :     Optional[Any]           =   json.loads(old_tokenizer._tokenizer.to_str()				)
										self.assertNotIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)






@require_tokenizers
class 						UpperCAmelCase	(		unittest.TestCase				):



			def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[Any]:


										'''simple docstring'''


										snake_case :     Tuple           =   PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"				)
										snake_case :     List[Any]           =   "The Matrix is a 1999 science fiction action film."

										with concurrent.futures.ThreadPoolExecutor() as executor:
																	snake_case :     Union[str, Any]           =   [executor.submit(self.fetch					,		snake_case__					,		snake_case__				) for i in range(10				)]
																	snake_case :     Optional[Any]           =   [future.result() for future in futures]
																	self.assertEqual(snake_case__					,		[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10				)]				)



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


										'''simple docstring'''


										return tokenizer.encode(snake_case__					,		truncation="longest_first"					,		padding="longest"				)







import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase							= []
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 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.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     UpperCamelCase      (					__lowerCamelCase  :				Dict       ,      __lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Any					):
       snake_case :     int           =   state_dict.pop(__lowerCamelCase					)
       snake_case :     int           =   val


def     UpperCamelCase      (					__lowerCamelCase  :				List[Any]					):
       snake_case :     Optional[int]           =   OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     snake_case :     List[str]           =   key.replace("backbone.0.body"       ,      "backbone.conv_encoder.model"					)
                     snake_case :     str           =   value
              else:
                     snake_case :     Any           =   value

       return new_state_dict


def     UpperCamelCase      (					__lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Dict=False					):
       snake_case :     List[str]           =   ""
       if is_panoptic:
              snake_case :     Dict           =   "detr."

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


def     UpperCamelCase      (					):
       snake_case :     Optional[Any]           =   "http://images.cocodataset.org/val2017/000000039769.jpg"
       snake_case :     List[str]           =   Image.open(requests.get(__lowerCamelCase       ,      stream=__lowerCamelCase					).raw					)

       return im


@torch.no_grad()
def     UpperCamelCase      (					__lowerCamelCase  :				Any       ,      __lowerCamelCase  :				List[Any]					):
       snake_case :     Tuple           =   DetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              snake_case :     Optional[Any]           =   "resnet101"
       if "dc5" in model_name:
              snake_case :     int           =   True
       snake_case :     List[str]           =   "panoptic" in model_name
       if is_panoptic:
              snake_case :     Dict           =   250
       else:
              snake_case :     int           =   91
              snake_case :     List[Any]           =   "huggingface/label-files"
              snake_case :     Dict           =   "coco-detection-id2label.json"
              snake_case :     Dict           =   json.load(open(hf_hub_download(__lowerCamelCase       ,      __lowerCamelCase       ,      repo_type="dataset"					)       ,      "r"					)					)
              snake_case :     Union[str, Any]           =   {int(__lowerCamelCase					): v for k, v in idalabel.items()}
              snake_case :     int           =   idalabel
              snake_case :     str           =   {v: k for k, v in idalabel.items()}

       # load image processor
       snake_case :     Tuple           =   "coco_panoptic" if is_panoptic else "coco_detection"
       snake_case :     str           =   DetrImageProcessor(format=__lowerCamelCase					)

       # prepare image
       snake_case :     Optional[int]           =   prepare_img()
       snake_case :     Optional[Any]           =   image_processor(images=__lowerCamelCase       ,      return_tensors="pt"					)
       snake_case :     Optional[int]           =   encoding["pixel_values"]

       logger.info(f"""Converting model {model_name}..."""					)

       # load original model from torch hub
       snake_case :     Dict           =   torch.hub.load("facebookresearch/detr"       ,      __lowerCamelCase       ,      pretrained=__lowerCamelCase					).eval()
       snake_case :     Any           =   detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     snake_case :     List[str]           =   "detr." + src
              rename_key(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					)
       snake_case :     str           =   rename_backbone_keys(__lowerCamelCase					)
       # query, key and value matrices need special treatment
       read_in_q_k_v(__lowerCamelCase       ,      is_panoptic=__lowerCamelCase					)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       snake_case :     List[Any]           =   "detr.model." if is_panoptic else "model."
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith("detr"					)
                         and not key.startswith("class_labels_classifier"					)
                         and not key.startswith("bbox_predictor"					)
                     ):
                            snake_case :     Tuple           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Any           =   val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            snake_case :     Dict           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Optional[Any]           =   val
                     elif key.startswith("bbox_attention"					) or key.startswith("mask_head"					):
                            continue
                     else:
                            snake_case :     Union[str, Any]           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     int           =   val
              else:
                     if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
                            snake_case :     int           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Optional[int]           =   val
    # finally, create HuggingFace model and load state dict
       snake_case :     Union[str, Any]           =   DetrForSegmentation(__lowerCamelCase					) if is_panoptic else DetrForObjectDetection(__lowerCamelCase					)
       model.load_state_dict(__lowerCamelCase					)
       model.eval()
       # verify our conversion
       snake_case :     List[Any]           =   detr(__lowerCamelCase					)
       snake_case :     Tuple           =   model(__lowerCamelCase					)
       assert torch.allclose(outputs.logits       ,      original_outputs["pred_logits"]       ,      atol=1E-4					)
       assert torch.allclose(outputs.pred_boxes       ,      original_outputs["pred_boxes"]       ,      atol=1E-4					)
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks       ,      original_outputs["pred_masks"]       ,      atol=1E-4					)

       # Save model and image processor
       logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""					)
       Path(__lowerCamelCase					).mkdir(exist_ok=__lowerCamelCase					)
       model.save_pretrained(__lowerCamelCase					)
       image_processor.save_pretrained(__lowerCamelCase					)


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

   parser.add_argument(
       """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the DETR model 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."""
   )
   __lowerCamelCase							= parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





