






import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A_       :Tuple       =						'''src/transformers'''
A_       :str       =						'''docs/source/en/tasks'''






def 							A     (       a_							,a_							,a_					)     ->						Optional[int]:
					with open(a_							,'r'							,encoding='utf-8'							,newline='\n'					) as f:
										__UpperCamelCase      :					Optional[int]       		=f.readlines()
					# Find the start prompt.
					__UpperCamelCase      :					Optional[Any]       		=0
					while not lines[start_index].startswith(a_					):
										start_index += 1
					start_index += 1

					__UpperCamelCase      :					Any       		=start_index
					while not lines[end_index].startswith(a_					):
										end_index += 1
					end_index -= 1

					while len(lines[start_index]					) <= 1:
										start_index += 1
					while len(lines[end_index]					) <= 1:
										end_index -= 1
					end_index += 1
					return "".join(lines[start_index:end_index]					), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
A_       :int       =						direct_transformers_import(TRANSFORMERS_PATH)

A_       :Union[str, Any]       =						{
    '''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    '''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    '''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    '''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    '''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    '''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    '''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    '''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    '''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    '''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    '''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    '''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    '''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    '''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A_       :Optional[Any]       =						{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}






def 							A     (       a_					)     ->						Tuple:
					__UpperCamelCase      :					int       		=TASK_GUIDE_TO_MODELS[task_guide]
					__UpperCamelCase      :					Dict       		=SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_							,set()					)
					__UpperCamelCase      :					List[Any]       		={
					    code: name
					    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
					    if (code in model_maping_names or code in special_model_types)
					}
					return ", ".join([F'[{name}](../model_doc/{code})' for code, name in model_names.items()]					) + "\n"






def 							A     (       a_							,a_=False					)     ->						Optional[int]:
					__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					Tuple       		=_find_text_in_file(
					    filename=os.path.join(a_							,a_					)							,start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'							,end_prompt='<!--End of the generated tip-->'							,)

					__UpperCamelCase      :					Optional[Any]       		=get_model_list_for_task(a_					)

					if current_list != new_list:
										if overwrite:
															with open(os.path.join(a_							,a_					)							,'w'							,encoding='utf-8'							,newline='\n'					) as f:
																				f.writelines(lines[:start_index] + [new_list] + lines[end_index:]					)
										else:
															raise ValueError(
															    F'The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'
															    ' to fix this.'					)


if __name__ == "__main__":
							A_       :List[Any]       =						argparse.ArgumentParser()
							parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
							A_       :int       =						parser.parse_args()

							for task_guide in TASK_GUIDE_TO_MODELS.keys():
														check_model_list_for_task(task_guide, args.fix_and_overwrite)






"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Dict											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[str]											=    -1

         _UpperCAmelCase							:  List[str]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[str]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  List[Any]											=    tokenizer.decode(greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  str											=    TextStreamer(A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  List[str]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[str]											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  List[Any]											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    -1

         _UpperCAmelCase							:  Union[str, Any]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  str											=    tokenizer.decode(greedy_ids[0]		)

         _UpperCAmelCase							:  Union[str, Any]											=    TextIteratorStreamer(A		)
         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Any											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()
         _UpperCAmelCase							:  Any											=    ''''''
         for new_text in streamer:
             streamer_text += new_text

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  str											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Any											=    -1

         _UpperCAmelCase							:  Dict											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  Dict											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  Dict											=    greedy_ids[:, input_ids.shape[1] :]
         _UpperCAmelCase							:  List[str]											=    tokenizer.decode(new_greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Any											=    TextStreamer(A       ,	skip_prompt=A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  Union[str, Any]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
         # with actual models -- the dummy models' tokenizers are not aligned with their models, and
         # `skip_special_tokens=True` has no effect on them
         _UpperCAmelCase							:  int											=    AutoTokenizer.from_pretrained('''distilgpt2'''		)
         _UpperCAmelCase							:  Union[str, Any]											=    AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(A		)
         _UpperCAmelCase							:  Tuple											=    -1

         _UpperCAmelCase							:  int											=    torch.ones((1, 5)       ,	device=A		).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Optional[Any]											=    TextStreamer(A       ,	skip_special_tokens=A		)
             model.generate(A       ,	max_new_tokens=1       ,	do_sample=A       ,	streamer=A		)

         # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
         # re-tokenized, must only contain one token
         _UpperCAmelCase							:  Tuple											=    cs.out[:-1]  # Remove the final "\n"
         _UpperCAmelCase							:  int											=    tokenizer(A       ,	return_tensors='''pt'''		)
         self.assertEqual(streamer_text_tokenized.input_ids.shape       ,	(1, 1)		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Any											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Dict											=    -1

         _UpperCAmelCase							:  str											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    TextIteratorStreamer(A       ,	timeout=0.001		)
         _UpperCAmelCase							:  Union[str, Any]											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Optional[Any]											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()

         # The streamer will timeout after 0.001 seconds, so an exception will be raised
         with self.assertRaises(A		):
             _UpperCAmelCase							:  Optional[Any]											=    ''''''
             for new_text in streamer:
                 streamer_text += new_text


