"""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		):



				def       _lowercase  (self       :   List[Any])					->			Dict:
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							Optional[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Any   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							str   	=				tokenizer.decode(greedy_ids[0])

					with CaptureStdout() as cs:
						__snake_case    :							List[str]   	=				TextStreamer(_A)
						model.generate(_A			,						max_new_tokens=10			,						do_sample=_A			,						streamer=_A)
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					__snake_case    :							Optional[Any]   	=				cs.out[:-1]

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   int)					->			Optional[Any]:
					__snake_case    :							Any   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							List[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Optional[int]   	=				-1

					__snake_case    :							str   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							int   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							int   	=				tokenizer.decode(greedy_ids[0])

					__snake_case    :							int   	=				TextIteratorStreamer(_A)
					__snake_case    :							List[Any]   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							Any   	=				Thread(target=model.generate			,						kwargs=_A)
					thread.start()
					__snake_case    :							Dict   	=				''
					for new_text in streamer:
						streamer_text += new_text

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   Any)					->			List[str]:
					__snake_case    :							List[str]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Any   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Optional[int]   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							Union[str, Any]   	=				greedy_ids[:, input_ids.shape[1] :]
					__snake_case    :							Optional[Any]   	=				tokenizer.decode(new_greedy_ids[0])

					with CaptureStdout() as cs:
						__snake_case    :							str   	=				TextStreamer(_A			,						skip_prompt=_A)
						model.generate(_A			,						max_new_tokens=10			,						do_sample=_A			,						streamer=_A)
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					__snake_case    :							List[str]   	=				cs.out[:-1]

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   List[str])					->			Dict:
					# 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
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('distilgpt2')
					__snake_case    :							Dict   	=				AutoModelForCausalLM.from_pretrained('distilgpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[str]   	=				torch.ones((1, 5)			,						device=_A).long() * model.config.bos_token_id
					with CaptureStdout() as cs:
						__snake_case    :							Tuple   	=				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
					__snake_case    :							Tuple   	=				cs.out[:-1]  # Remove the final "\n"
					__snake_case    :							int   	=				tokenizer(_A			,						return_tensors='pt')
					self.assertEqual(streamer_text_tokenized.input_ids.shape			,						(1, 1))






				def       _lowercase  (self       :   Optional[int])					->			List[str]:
					__snake_case    :							Optional[int]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[int]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							int   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							List[Any]   	=				TextIteratorStreamer(_A			,						timeout=0.001)
					__snake_case    :							str   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							str   	=				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):
						__snake_case    :							Any   	=				''
						for new_text in streamer:
							streamer_text += new_text






"""simple docstring"""







import argparse
from collections import defaultdict

import yaml


_a  :  Tuple=				"docs/source/en/_toctree.yml"
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				defaultdict(UpperCAmelCase_						)
	__snake_case    :							List[str]   	=				[]
	__snake_case    :							Tuple   	=				[]
	for doc in doc_list:
		if "local" in doc:
			counts[doc["local"]] += 1

		if doc["title"].lower() == "overview":
			overview_doc.append({'local': doc['local'], 'title': doc['title']}						)
		else:
			new_doc_list.append(UpperCAmelCase_						)

	__snake_case    :							Any   	=				new_doc_list
	__snake_case    :							Dict   	=				[key for key, value in counts.items() if value > 1]

	__snake_case    :							Optional[int]   	=				[]
	for duplicate_key in duplicates:
		__snake_case    :							Tuple   	=				list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}						)
		if len(UpperCAmelCase_						) > 1:
			raise ValueError(
			    F"{duplicate_key} is present several times in the documentation table of content at "
			    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
			    'others.'						)
		# Only add this once
		new_doc.append({'local': duplicate_key, 'title': titles[0]}						)

	# Add none duplicate-keys
	new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]						)
	__snake_case    :							List[Any]   	=				sorted(UpperCAmelCase_    ,							key=lambda UpperCAmelCase_						: s["title"].lower()						)

	# "overview" gets special treatment and is always first
	if len(UpperCAmelCase_						) > 1:
		raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'						)

	overview_doc.extend(UpperCAmelCase_						)

	# Sort
	return overview_doc
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]=False						)							->    str:

	'''simple docstring'''





	with open(UpperCAmelCase_    ,							encoding='utf-8'						) as f:
		__snake_case    :							List[Any]   	=				yaml.safe_load(f.read()						)

	# Get to the API doc
	__snake_case    :							str   	=				0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	__snake_case    :							Union[str, Any]   	=				content[api_idx]['sections']

	# Then to the model doc
	__snake_case    :							Dict   	=				0
	while api_doc[scheduler_idx]["title"] != "Schedulers":
		scheduler_idx += 1

	__snake_case    :							Optional[Any]   	=				api_doc[scheduler_idx]['sections']
	__snake_case    :							Tuple   	=				clean_doc_toc(UpperCAmelCase_						)

	__snake_case    :							Tuple   	=				False
	if new_scheduler_doc != scheduler_doc:
		__snake_case    :							Optional[int]   	=				True
		if overwrite:
			__snake_case    :							Tuple   	=				new_scheduler_doc

	if diff:
		if overwrite:
			__snake_case    :							Optional[int]   	=				api_doc
			with open(UpperCAmelCase_    ,							'w'    ,							encoding='utf-8'						) as f:
				f.write(yaml.dump(UpperCAmelCase_    ,							allow_unicode=UpperCAmelCase_						)						)
		else:
			raise ValueError(
			    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Any=False						)							->    Union[str, Any]:

	'''simple docstring'''





	with open(UpperCAmelCase_    ,							encoding='utf-8'						) as f:
		__snake_case    :							Dict   	=				yaml.safe_load(f.read()						)

	# Get to the API doc
	__snake_case    :							Dict   	=				0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	__snake_case    :							Dict   	=				content[api_idx]['sections']

	# Then to the model doc
	__snake_case    :							Dict   	=				0
	while api_doc[pipeline_idx]["title"] != "Pipelines":
		pipeline_idx += 1

	__snake_case    :							Optional[int]   	=				False
	__snake_case    :							Optional[Any]   	=				api_doc[pipeline_idx]['sections']
	__snake_case    :							Tuple   	=				[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			__snake_case    :							List[str]   	=				pipeline_doc['section']
			__snake_case    :							Optional[int]   	=				clean_doc_toc(UpperCAmelCase_						)
			if overwrite:
				__snake_case    :							List[Any]   	=				new_sub_pipeline_doc
		new_pipeline_docs.append(UpperCAmelCase_						)

	# sort overall pipeline doc
	__snake_case    :							List[Any]   	=				clean_doc_toc(UpperCAmelCase_						)

	if new_pipeline_docs != pipeline_docs:
		__snake_case    :							Dict   	=				True
		if overwrite:
			__snake_case    :							Tuple   	=				new_pipeline_docs

	if diff:
		if overwrite:
			__snake_case    :							Union[str, Any]   	=				api_doc
			with open(UpperCAmelCase_    ,							'w'    ,							encoding='utf-8'						) as f:
				f.write(yaml.dump(UpperCAmelCase_    ,							allow_unicode=UpperCAmelCase_						)						)
		else:
			raise ValueError(
			    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'						)


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

	check_scheduler_doc(args.fix_and_overwrite)
	check_pipeline_doc(args.fix_and_overwrite)





