



import argparse
import collections
import importlib.util
import os
import re


# 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
lowerCamelCase__     =	"""src/diffusers"""
lowerCamelCase__     =	"""docs/source/en"""
lowerCamelCase__     =	"""."""

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Any			,       _SCREAMING_SNAKE_CASE    :     str			,       _SCREAMING_SNAKE_CASE    :     Optional[int]      ):
				"""simple docstring"""







				with open(_SCREAMING_SNAKE_CASE			,       """r"""			,       encoding="""utf-8"""			,       newline="""\n"""      ) as f:
								__a					       =						f.readlines()
				# Find the start prompt.
				__a					       =						0
				while not lines[start_index].startswith(_SCREAMING_SNAKE_CASE      ):
								start_index += 1
				start_index += 1

				__a					       =						start_index
				while not lines[end_index].startswith(_SCREAMING_SNAKE_CASE      ):
								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 |
lowerCamelCase__     =	"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
lowerCamelCase__     =	re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowerCamelCase__     =	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.
lowerCamelCase__     =	re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
lowerCamelCase__     =	importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
lowerCamelCase__     =	spec.loader.load_module()

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				__a					       =						re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""			,       _SCREAMING_SNAKE_CASE      )
				return [m.group(0      ) for m in matches]

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[str]			,       _SCREAMING_SNAKE_CASE    :     str      ):
				"""simple docstring"""







				__a					       =						2 if text == """✅""" or text == """❌""" else len(_SCREAMING_SNAKE_CASE      )
				__a					       =						(width - text_length) // 2
				__a					       =						width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent

def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				__a					       =						{
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				__a					       =						{name: config.replace("""ConfigMixin"""			,       """"""      ) 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.
				__a					       =						collections.defaultdict(_SCREAMING_SNAKE_CASE      )
				__a					       =						collections.defaultdict(_SCREAMING_SNAKE_CASE      )
				__a					       =						collections.defaultdict(_SCREAMING_SNAKE_CASE      )
				__a					       =						collections.defaultdict(_SCREAMING_SNAKE_CASE      )
				__a					       =						collections.defaultdict(_SCREAMING_SNAKE_CASE      )

				# Let's lookup through all diffusers object (once).
				for attr_name in dir(_SCREAMING_SNAKE_CASE      ):
								__a					       =						None
								if attr_name.endswith("""Tokenizer"""      ):
												__a					       =						slow_tokenizers
												__a					       =						attr_name[:-9]
								elif attr_name.endswith("""TokenizerFast"""      ):
												__a					       =						fast_tokenizers
												__a					       =						attr_name[:-13]
								elif _re_tf_models.match(_SCREAMING_SNAKE_CASE      ) is not None:
												__a					       =						tf_models
												__a					       =						_re_tf_models.match(_SCREAMING_SNAKE_CASE      ).groups()[0]
								elif _re_flax_models.match(_SCREAMING_SNAKE_CASE      ) is not None:
												__a					       =						flax_models
												__a					       =						_re_flax_models.match(_SCREAMING_SNAKE_CASE      ).groups()[0]
								elif _re_pt_models.match(_SCREAMING_SNAKE_CASE      ) is not None:
												__a					       =						pt_models
												__a					       =						_re_pt_models.match(_SCREAMING_SNAKE_CASE      ).groups()[0]

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

    # Let's build that table!
				__a					       =						list(model_name_to_config.keys()      )
				model_names.sort(key=str.lower      )
				__a					       =						["""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).
				__a					       =						[len(_SCREAMING_SNAKE_CASE      ) + 2 for c in columns]
				__a					       =						max([len(_SCREAMING_SNAKE_CASE      ) for name in model_names]      ) + 2

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

				__a					       =						{True: """✅""", False: """❌"""}
				for name in model_names:
								__a					       =						model_name_to_prefix[name]
								__a					       =						[
								    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(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      ) for l, w in zip(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )]      ) + "|\n"
				return table

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Union[str, Any]=False      ):
				"""simple docstring"""







				__a       ,   __a       ,   __a       ,   __a					       =						_find_text_in_file(
				    filename=os.path.join(_SCREAMING_SNAKE_CASE			,       """index.mdx"""      )			,       start_prompt="""<!--This table is updated automatically from the auto modules"""			,       end_prompt="""<!-- End table-->"""			,       )
				__a					       =						get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(_SCREAMING_SNAKE_CASE			,       """index.mdx"""      )			,       """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.mdx` has not been updated. Run `make fix-copies` to fix this."""      )


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

						check_model_table(args.fix_and_overwrite)





import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Dict=None			,       _SCREAMING_SNAKE_CASE    :     List[str]=None      ):
				"""simple docstring"""







				return field(default_factory=lambda: default			,       metadata=_SCREAMING_SNAKE_CASE      )






@dataclass
class 				SCREAMING_SNAKE_CASE     :
							__lowerCamelCase		:		str      =field(
							    metadata={'help': 'The csv file to plot.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Disable logarithmic scale when plotting'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={
							        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
							    }     , )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}     , )
							__lowerCamelCase		:		Optional[List[str]]      =list_field(
							    default=lowerCamelCase__     , metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}   )



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Any      ):
				"""simple docstring"""







				try:
								int(_SCREAMING_SNAKE_CASE      )
								return True
				except ValueError:
								return False

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







				try:
								float(_SCREAMING_SNAKE_CASE      )
								return True
				except ValueError:
								return False






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Optional[int] ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						args
											__a					       =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

											with open(self.args.csv_file ,			newline=""""""		) as csv_file:
															__a					       =						csv.DictReader(__lowercase		)
															for row in reader:
																			__a					       =						row["""model"""]
																			self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]		)		)
																			self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]		)		)
																			if can_convert_to_int(row["""result"""]		):
																							# value is not None
																							__a					       =						int(row["""result"""]		)
																			elif can_convert_to_float(row["""result"""]		):
																							# value is not None
																							__a					       =						float(row["""result"""]		)







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a       ,   __a					       =						plt.subplots()
											__a					       =						"""Time usage""" if self.args.is_time else """Memory usage"""
											__a					       =						title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

											if not self.args.no_log_scale:
															# set logarithm scales
															ax.set_xscale("""log"""		)
															ax.set_yscale("""log"""		)

											for axis in [ax.xaxis, ax.yaxis]:
															axis.set_major_formatter(ScalarFormatter()		)

											for model_name_idx, model_name in enumerate(self.result_dict.keys()		):
															__a					       =						sorted(set(self.result_dict[model_name]["""bsz"""]		)		)
															__a					       =						sorted(set(self.result_dict[model_name]["""seq_len"""]		)		)
															__a					       =						self.result_dict[model_name]["""result"""]

															((__a)       ,   (__a))					       =						(
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															__a					       =						(
															    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
															)

															for inner_loop_value in inner_loop_array:
																			if self.args.plot_along_batch:
																							__a					       =						np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,			dtype=__lowercase ,			)
																			else:
																							__a					       =						np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,			dtype=np.floataa ,			)

																			((__a)       ,   (__a))					       =						(
																			    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																			)

																			__a					       =						np.asarray(__lowercase ,			__lowercase		)[: len(__lowercase		)]
																			plt.scatter(
																			    __lowercase ,			__lowercase ,			label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"		)
																			plt.plot(__lowercase ,			__lowercase ,			"""--"""		)

															title_str += F" {label_model_name} vs."

											__a					       =						title_str[:-4]
											__a					       =						"""Time in s""" if self.args.is_time else """Memory in MB"""

											# plot
											plt.title(__lowercase		)
											plt.xlabel(__lowercase		)
											plt.ylabel(__lowercase		)
											plt.legend()

											if self.args.figure_png_file is not None:
															plt.savefig(self.args.figure_png_file		)
											else:
															plt.show()



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						HfArgumentParser(_SCREAMING_SNAKE_CASE      )
				__a					       =						parser.parse_args_into_dataclasses()[0]
				__a					       =						Plot(args=_SCREAMING_SNAKE_CASE      )
				plot.plot()


if __name__ == "__main__":
						main()
