
import argparse
import collections
import os
import re

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_table.py
A			      =      """src/transformers"""
A			      =      """docs/source/en"""
A			      =      """."""





def    UpperCAmelCase							(  UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							List[Any]):







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

				lowerCamelCase					:			List[Any]									= start_index
				while not lines[end_index].startswith(UpperCAmelCase__):
								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


# Add here suffixes that are used to identify models, separated by |
A			      =      """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A			      =      re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
A			      =      re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
A			      =      re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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





def    UpperCAmelCase							(  UpperCAmelCase__							:							Union[str, Any]):
				lowerCamelCase					:			Optional[int]									= re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'       ,							UpperCAmelCase__)
				return [m.group(0) for m in matches]





def    UpperCAmelCase							(  UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Union[str, Any]):
				lowerCamelCase					:			List[str]									= 2 if text == "✅" or text == "❌" else len(UpperCAmelCase__)
				lowerCamelCase					:			List[Any]									= (width - text_length) // 2
				lowerCamelCase					:			List[str]									= width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			int									= transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCamelCase					:			Union[str, Any]									= {
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				lowerCamelCase					:			List[str]									= {name: config.replace('Config'       ,							'') for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				lowerCamelCase					:			Tuple									= collections.defaultdict(UpperCAmelCase__)
				lowerCamelCase					:			Optional[Any]									= collections.defaultdict(UpperCAmelCase__)
				lowerCamelCase					:			int									= collections.defaultdict(UpperCAmelCase__)
				lowerCamelCase					:			List[str]									= collections.defaultdict(UpperCAmelCase__)
				lowerCamelCase					:			List[str]									= collections.defaultdict(UpperCAmelCase__)

				# Let's lookup through all transformers object (once).
				for attr_name in dir(UpperCAmelCase__):
								lowerCamelCase					:			Union[str, Any]									= None
								if attr_name.endswith('Tokenizer'):
												lowerCamelCase					:			int									= slow_tokenizers
												lowerCamelCase					:			Any									= attr_name[:-9]
								elif attr_name.endswith('TokenizerFast'):
												lowerCamelCase					:			int									= fast_tokenizers
												lowerCamelCase					:			List[Any]									= attr_name[:-13]
								elif _re_tf_models.match(UpperCAmelCase__) is not None:
												lowerCamelCase					:			List[str]									= tf_models
												lowerCamelCase					:			List[Any]									= _re_tf_models.match(UpperCAmelCase__).groups()[0]
								elif _re_flax_models.match(UpperCAmelCase__) is not None:
												lowerCamelCase					:			int									= flax_models
												lowerCamelCase					:			Any									= _re_flax_models.match(UpperCAmelCase__).groups()[0]
								elif _re_pt_models.match(UpperCAmelCase__) is not None:
												lowerCamelCase					:			Tuple									= pt_models
												lowerCamelCase					:			int									= _re_pt_models.match(UpperCAmelCase__).groups()[0]

								if lookup_dict is not None:
												while len(UpperCAmelCase__) > 0:
																if attr_name in model_name_to_prefix.values():
																				lowerCamelCase					:			Any									= True
																				break
																# Try again after removing the last word in the name
																lowerCamelCase					:			Dict									= "".join(camel_case_split(UpperCAmelCase__)[:-1])

								# Let's build that table!
				lowerCamelCase					:			Tuple									= list(model_name_to_config.keys())
				model_names.sort(key=str.lower)
				lowerCamelCase					:			Tuple									= ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				lowerCamelCase					:			List[Any]									= [len(UpperCAmelCase__) + 2 for c in columns]
				lowerCamelCase					:			List[str]									= max([len(UpperCAmelCase__) for name in model_names]) + 2

				# Build the table per se
				lowerCamelCase					:			List[str]									= "|" + "|".join([_center_text(UpperCAmelCase__       ,							UpperCAmelCase__) for c, w in zip(UpperCAmelCase__       ,							UpperCAmelCase__)]) + "|\n"
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]) + "|\n"

				lowerCamelCase					:			str									= {True: "✅", False: "❌"}
				for name in model_names:
								lowerCamelCase					:			Any									= model_name_to_prefix[name]
								lowerCamelCase					:			List[str]									= [
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(UpperCAmelCase__       ,							UpperCAmelCase__) for l, w in zip(UpperCAmelCase__       ,							UpperCAmelCase__)]) + "|\n"
				return table





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]=False):
				lowerCamelCase					:			Optional[int]									= _find_text_in_file(
				    filename=os.path.join(UpperCAmelCase__       ,							'index.md')       ,							start_prompt='<!--This table is updated automatically from the auto modules'       ,							end_prompt='<!-- End table-->'       ,							)
				lowerCamelCase					:			Optional[Any]									= get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(UpperCAmelCase__       ,							'index.md')       ,							'w'       ,							encoding='utf-8'       ,							newline='\n') as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:])
								else:
												raise ValueError(
												    'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.')


if __name__ == "__main__":
					A			      =      argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					A			      =      parser.parse_args()

					check_model_table(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     __snake_case		(   unittest.TestCase):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			Optional[int]									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Union[str, Any]									= -1

									lowerCamelCase					:			Optional[Any]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			List[Any]									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			int									= tokenizer.decode(greedy_ids[0]      )

									with CaptureStdout() as cs:
													lowerCamelCase					:			Optional[int]									= 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
									lowerCamelCase					:			int									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

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

									lowerCamelCase					:			Union[str, Any]									= TextIteratorStreamer(A      )
									lowerCamelCase					:			Tuple									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			List[Any]									= Thread(target=model.generate,    kwargs=A      )
									thread.start()
									lowerCamelCase					:			List[Any]									= ''
									for new_text in streamer:
													streamer_text += new_text

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			int									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Optional[Any]									= -1

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

									with CaptureStdout() as cs:
													lowerCamelCase					:			Tuple									= 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
									lowerCamelCase					:			Optional[int]									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= AutoTokenizer.from_pretrained('distilgpt2'      )
									lowerCamelCase					:			Dict									= AutoModelForCausalLM.from_pretrained('distilgpt2'      ).to(A      )
									lowerCamelCase					:			Dict									= -1

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




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			List[str]									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Any									= -1

									lowerCamelCase					:			int									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			int									= TextIteratorStreamer(A,    timeout=0.001      )
									lowerCamelCase					:			Any									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			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      ):
													lowerCamelCase					:			Dict									= ''
													for new_text in streamer:
																	streamer_text += new_text
