


import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
			from test_module.custom_tokenization_fast import CustomTokenizerFast






class      UpperCamelCase					(		unittest.TestCase     ):


			'''simple docstring'''







			def 		UpperCamelCase  (     self		):
								lowercase_       :int     =       0
			@slow
			def 		UpperCamelCase  (     self		):
								for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
													lowercase_       :Tuple     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
													self.assertIsNotNone(UpperCamelCase_		)
													self.assertIsInstance(UpperCamelCase_   ,							(BertTokenizer, BertTokenizerFast)		)
													self.assertGreater(len(UpperCamelCase_		)   ,							0		)

								for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
													lowercase_       :Union[str, Any]     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
													self.assertIsNotNone(UpperCamelCase_		)
													self.assertIsInstance(UpperCamelCase_   ,							(GPTaTokenizer, GPTaTokenizerFast)		)
													self.assertGreater(len(UpperCamelCase_		)   ,							0		)
			def 		UpperCamelCase  (     self		):
								lowercase_       :str     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
								self.assertIsInstance(UpperCamelCase_   ,							(BertTokenizer, BertTokenizerFast)		)
								self.assertEqual(tokenizer.vocab_size   ,							12		)
			def 		UpperCamelCase  (     self		):
								lowercase_       :str     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
								self.assertIsInstance(UpperCamelCase_   ,							(RobertaTokenizer, RobertaTokenizerFast)		)
								self.assertEqual(tokenizer.vocab_size   ,							20		)
			def 		UpperCamelCase  (     self		):
								lowercase_       :List[Any]     =       AutoConfig.from_pretrained(UpperCamelCase_		)
								self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)
								# Check that tokenizer_type ≠ model_type
								lowercase_       :int     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							config=UpperCamelCase_		)
								self.assertIsInstance(UpperCamelCase_   ,							(BertTokenizer, BertTokenizerFast)		)
								self.assertEqual(tokenizer.vocab_size   ,							12		)
			def 		UpperCamelCase  (     self		):
								with tempfile.TemporaryDirectory() as tmp_dir:
													shutil.copy('''./tests/fixtures/vocab.txt'''   ,							os.path.join(UpperCamelCase_   ,							'''vocab.txt'''		)		)

													lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							tokenizer_type='''bert'''   ,							use_fast=UpperCamelCase_		)
													self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)

								with tempfile.TemporaryDirectory() as tmp_dir:
													shutil.copy('''./tests/fixtures/vocab.json'''   ,							os.path.join(UpperCamelCase_   ,							'''vocab.json'''		)		)
													shutil.copy('''./tests/fixtures/merges.txt'''   ,							os.path.join(UpperCamelCase_   ,							'''merges.txt'''		)		)

													lowercase_       :List[str]     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							tokenizer_type='''gpt2'''   ,							use_fast=UpperCamelCase_		)
													self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)
			@require_tokenizers
			def 		UpperCamelCase  (     self		):
								with tempfile.TemporaryDirectory() as tmp_dir:
													shutil.copy('''./tests/fixtures/vocab.txt'''   ,							os.path.join(UpperCamelCase_   ,							'''vocab.txt'''		)		)

													lowercase_       :Any     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							tokenizer_type='''bert'''		)
													self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)

								with tempfile.TemporaryDirectory() as tmp_dir:
													shutil.copy('''./tests/fixtures/vocab.json'''   ,							os.path.join(UpperCamelCase_   ,							'''vocab.json'''		)		)
													shutil.copy('''./tests/fixtures/merges.txt'''   ,							os.path.join(UpperCamelCase_   ,							'''merges.txt'''		)		)

													lowercase_       :Any     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							tokenizer_type='''gpt2'''		)
													self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)
			def 		UpperCamelCase  (     self		):
								with pytest.raises(UpperCamelCase_		):
													AutoTokenizer.from_pretrained('''./'''   ,							tokenizer_type='''xxx'''		)
			@require_tokenizers
			def 		UpperCamelCase  (     self		):
								for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
													lowercase_       :Dict     =       tokenizer_class.from_pretrained('''wietsedv/bert-base-dutch-cased'''		)
													self.assertIsInstance(UpperCamelCase_   ,							(BertTokenizer, BertTokenizerFast)		)

													if isinstance(UpperCamelCase_   ,							UpperCamelCase_		):
																		self.assertEqual(tokenizer.basic_tokenizer.do_lower_case   ,							UpperCamelCase_		)
													else:
																		self.assertEqual(tokenizer.do_lower_case   ,							UpperCamelCase_		)

													self.assertEqual(tokenizer.model_max_length   ,							512		)
			@require_tokenizers
			def 		UpperCamelCase  (     self		):
								for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
													with self.assertRaisesRegex(
													    UpperCamelCase_   ,							'''julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'''   ,							):
																		lowercase_       :Optional[Any]     =       tokenizer_class.from_pretrained('''julien-c/herlolip-not-exists'''		)
			def 		UpperCamelCase  (     self		):
								# tests: https://github.com/huggingface/transformers/pull/13251
								# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
								# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
								lowercase_       :Any     =       TOKENIZER_MAPPING.values()
								lowercase_       :str     =       []

								for slow_tok, fast_tok in tokenizers:
													if slow_tok is not None:
																		tokenizer_names.append(slow_tok.__name__		)

													if fast_tok is not None:
																		tokenizer_names.append(fast_tok.__name__		)

								for tokenizer_name in tokenizer_names:
													# must find the right class
													tokenizer_class_from_name(UpperCamelCase_		)
			@require_tokenizers
			def 		UpperCamelCase  (     self		):
								self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''   ,							use_fast=UpperCamelCase_		)   ,							UpperCamelCase_		)
								self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''		)   ,							UpperCamelCase_		)
			@require_tokenizers
			def 		UpperCamelCase  (     self		):
								lowercase_       :Any     =       AutoTokenizer.from_pretrained('''distilbert-base-uncased'''   ,							do_lower_case=UpperCamelCase_		)
								lowercase_       :Tuple     =       '''Hello, world. How are you?'''
								lowercase_       :Optional[int]     =       tokenizer.tokenize(UpperCamelCase_		)
								self.assertEqual('''[UNK]'''   ,							tokens[0]		)

								lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained('''microsoft/mpnet-base'''   ,							do_lower_case=UpperCamelCase_		)
								lowercase_       :Dict     =       tokenizer.tokenize(UpperCamelCase_		)
								self.assertEqual('''[UNK]'''   ,							tokens[0]		)
			@require_tokenizers
			def 		UpperCamelCase  (     self		):
								lowercase_       :str     =       AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config'''		)
								self.assertEqual(type(UpperCamelCase_		)   ,							UpperCamelCase_		)
								self.assertEqual(tokenizer.model_max_length   ,							512		)
								self.assertEqual(tokenizer.vocab_size   ,							3_0000		)
								self.assertEqual(tokenizer.unk_token   ,							'''[UNK]'''		)
								self.assertEqual(tokenizer.padding_side   ,							'''right'''		)
								self.assertEqual(tokenizer.truncation_side   ,							'''right'''		)
			def 		UpperCamelCase  (     self		):
								lowercase_       :List[Any]     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
								self.assertIsInstance(UpperCamelCase_   ,							(BertTokenizer, BertTokenizerFast)		)
								with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer.save_pretrained(UpperCamelCase_		)
													lowercase_       :int     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)

								self.assertIsInstance(UpperCamelCase_   ,							tokenizer.__class__		)
								self.assertEqual(tokenizera.vocab_size   ,							12		)
			def 		UpperCamelCase  (     self		):
								lowercase_       :Optional[int]     =       AutoTokenizer.from_pretrained('''ctrl'''		)
								# There is no fast CTRL so this always gives us a slow tokenizer.
								self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)
			def 		UpperCamelCase  (     self		):
								# Check we can load the tokenizer config of an online model.
								lowercase_       :Union[str, Any]     =       get_tokenizer_config('''bert-base-cased'''		)
								lowercase_       :Dict     =       config.pop('''_commit_hash'''   ,							UpperCamelCase_		)
								# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
								self.assertEqual(UpperCamelCase_   ,							{'''do_lower_case''': False}		)

								# This model does not have a tokenizer_config so we get back an empty dict.
								lowercase_       :Dict     =       get_tokenizer_config(UpperCamelCase_		)
								self.assertDictEqual(UpperCamelCase_   ,							{}		)

								# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
								lowercase_       :Tuple     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
								with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer.save_pretrained(UpperCamelCase_		)
													lowercase_       :Optional[Any]     =       get_tokenizer_config(UpperCamelCase_		)

								# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
								self.assertEqual(config['''tokenizer_class''']   ,							'''BertTokenizer'''		)
			def 		UpperCamelCase  (     self		):
								try:
													AutoConfig.register('''custom'''   ,							UpperCamelCase_		)

													AutoTokenizer.register(UpperCamelCase_   ,							slow_tokenizer_class=UpperCamelCase_		)
													# Trying to register something existing in the Transformers library will raise an error
													with self.assertRaises(UpperCamelCase_		):
																		AutoTokenizer.register(UpperCamelCase_   ,							slow_tokenizer_class=UpperCamelCase_		)

													lowercase_       :List[Any]     =       CustomTokenizer.from_pretrained(UpperCamelCase_		)
													with tempfile.TemporaryDirectory() as tmp_dir:
																		tokenizer.save_pretrained(UpperCamelCase_		)

																		lowercase_       :Tuple     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
																		self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)

								finally:
													if "custom" in CONFIG_MAPPING._extra_content:
																		del CONFIG_MAPPING._extra_content["custom"]
													if CustomConfig in TOKENIZER_MAPPING._extra_content:
																		del TOKENIZER_MAPPING._extra_content[CustomConfig]
			@require_tokenizers
			def 		UpperCamelCase  (     self		):
								try:
													AutoConfig.register('''custom'''   ,							UpperCamelCase_		)

													# Can register in two steps
													AutoTokenizer.register(UpperCamelCase_   ,							slow_tokenizer_class=UpperCamelCase_		)
													self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,							(CustomTokenizer, None)		)
													AutoTokenizer.register(UpperCamelCase_   ,							fast_tokenizer_class=UpperCamelCase_		)
													self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,							(CustomTokenizer, CustomTokenizerFast)		)

													del TOKENIZER_MAPPING._extra_content[CustomConfig]
													# Can register in one step
													AutoTokenizer.register(
													    UpperCamelCase_   ,							slow_tokenizer_class=UpperCamelCase_   ,							fast_tokenizer_class=UpperCamelCase_		)
													self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,							(CustomTokenizer, CustomTokenizerFast)		)

													# Trying to register something existing in the Transformers library will raise an error
													with self.assertRaises(UpperCamelCase_		):
																		AutoTokenizer.register(UpperCamelCase_   ,							fast_tokenizer_class=UpperCamelCase_		)

													# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
													# and that model does not have a tokenizer.json
													with tempfile.TemporaryDirectory() as tmp_dir:
																		lowercase_       :Optional[Any]     =       BertTokenizerFast.from_pretrained(UpperCamelCase_		)
																		bert_tokenizer.save_pretrained(UpperCamelCase_		)
																		lowercase_       :str     =       CustomTokenizerFast.from_pretrained(UpperCamelCase_		)

													with tempfile.TemporaryDirectory() as tmp_dir:
																		tokenizer.save_pretrained(UpperCamelCase_		)

																		lowercase_       :Tuple     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
																		self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)

																		lowercase_       :Dict     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
																		self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)

								finally:
													if "custom" in CONFIG_MAPPING._extra_content:
																		del CONFIG_MAPPING._extra_content["custom"]
													if CustomConfig in TOKENIZER_MAPPING._extra_content:
																		del TOKENIZER_MAPPING._extra_content[CustomConfig]
			def 		UpperCamelCase  (     self		):
								# If remote code is not set, we will time out when asking whether to load the model.
								with self.assertRaises(UpperCamelCase_		):
													lowercase_       :List[Any]     =       AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''		)
								# If remote code is disabled, we can't load this config.
								with self.assertRaises(UpperCamelCase_		):
													lowercase_       :Tuple     =       AutoTokenizer.from_pretrained(
													    '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_		)

								lowercase_       :List[str]     =       AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_		)
								self.assertTrue(tokenizer.special_attribute_present		)
								# Test tokenizer can be reloaded.
								with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer.save_pretrained(UpperCamelCase_		)
													lowercase_       :Any     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							trust_remote_code=UpperCamelCase_		)
								self.assertTrue(reloaded_tokenizer.special_attribute_present		)

								if is_tokenizers_available():
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)
													self.assertEqual(reloaded_tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)

													# Test we can also load the slow version
													lowercase_       :List[str]     =       AutoTokenizer.from_pretrained(
													    '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
													self.assertTrue(tokenizer.special_attribute_present		)
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
													# Test tokenizer can be reloaded.
													with tempfile.TemporaryDirectory() as tmp_dir:
																		tokenizer.save_pretrained(UpperCamelCase_		)
																		lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							trust_remote_code=UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
													self.assertEqual(reloaded_tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
													self.assertTrue(reloaded_tokenizer.special_attribute_present		)
								else:
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
													self.assertEqual(reloaded_tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
			@require_tokenizers
			def 		UpperCamelCase  (     self		):






								class      UpperCamelCase					(		lowercase__     ):


											'''simple docstring'''







											lowercase					:	List[str]									=False






								class      UpperCamelCase					(		lowercase__     ):


											'''simple docstring'''







											lowercase					:	Any									=NewTokenizer
											lowercase					:	Union[str, Any]									=False

								try:
													AutoConfig.register('''custom'''   ,							UpperCamelCase_		)
													AutoTokenizer.register(UpperCamelCase_   ,							slow_tokenizer_class=UpperCamelCase_		)
													AutoTokenizer.register(UpperCamelCase_   ,							fast_tokenizer_class=UpperCamelCase_		)
													# If remote code is not set, the default is to use local
													lowercase_       :List[Any]     =       AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''		)
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)
													self.assertFalse(tokenizer.special_attribute_present		)
													lowercase_       :Any     =       AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''   ,							use_fast=UpperCamelCase_		)
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
													self.assertFalse(tokenizer.special_attribute_present		)

													# If remote code is disabled, we load the local one.
													lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(
													    '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_		)
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)
													self.assertFalse(tokenizer.special_attribute_present		)
													lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(
													    '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
													self.assertFalse(tokenizer.special_attribute_present		)

													# If remote is enabled, we load from the Hub
													lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(
													    '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_		)
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)
													self.assertTrue(tokenizer.special_attribute_present		)
													lowercase_       :List[Any]     =       AutoTokenizer.from_pretrained(
													    '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
													self.assertTrue(tokenizer.special_attribute_present		)

								finally:
													if "custom" in CONFIG_MAPPING._extra_content:
																		del CONFIG_MAPPING._extra_content["custom"]
													if CustomConfig in TOKENIZER_MAPPING._extra_content:
																		del TOKENIZER_MAPPING._extra_content[CustomConfig]
			def 		UpperCamelCase  (     self		):
								lowercase_       :List[str]     =       AutoTokenizer.from_pretrained(
								    '''hf-internal-testing/test_dynamic_tokenizer_legacy'''   ,							trust_remote_code=UpperCamelCase_		)
								self.assertTrue(tokenizer.special_attribute_present		)
								if is_tokenizers_available():
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)

													# Test we can also load the slow version
													lowercase_       :Any     =       AutoTokenizer.from_pretrained(
													    '''hf-internal-testing/test_dynamic_tokenizer_legacy'''   ,							trust_remote_code=UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
													self.assertTrue(tokenizer.special_attribute_present		)
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
								else:
													self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
			def 		UpperCamelCase  (     self		):
								with self.assertRaisesRegex(
								    UpperCamelCase_   ,							'''bert-base is not a local folder and is not a valid model identifier'''		):
													lowercase_       :List[str]     =       AutoTokenizer.from_pretrained('''bert-base'''		)
			def 		UpperCamelCase  (     self		):
								with self.assertRaisesRegex(
								    UpperCamelCase_   ,							R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''		):
													lowercase_       :int     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							revision='''aaaaaa'''		)




			def 		UpperCamelCase  (     self		):
								# Make sure we have cached the tokenizer.
								lowercase_       :List[Any]     =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)
								with RequestCounter() as counter:
													lowercase_       :Optional[int]     =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)
													self.assertEqual(counter.get_request_count   ,							0		)
													self.assertEqual(counter.head_request_count   ,							1		)
													self.assertEqual(counter.other_request_count   ,							0		)





import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def     UpperCamelCase    (      _a       ,		_a       ,		_a=None		)					->							Dict:
     '''simple docstring'''





     assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
     lowercase_       :str     =       nn.Parameter(_a		)
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
          lowercase_       :List[str]     =       nn.Parameter(_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							List[str]:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       np.asarray(weights[0]		)
     lowercase_       :str     =       np.asarray(weights[1]		)
     lowercase_       :Union[str, Any]     =       np.asarray(weights[2]		)

     set_param(
         torch_layer.self_attention.query_key       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.value       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.output.dense       ,		torch.tensor(_a		).view(-1       ,		_a		).contiguous().transpose(0       ,		1		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[int]:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       np.asarray(weights[0]		)
     lowercase_       :Tuple     =       np.asarray(weights[1]		)
     lowercase_       :List[Any]     =       np.asarray(weights[2]		)
     lowercase_       :Optional[int]     =       np.asarray(weights[3]		)

     set_param(
         torch_layer.self_attention.query       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.key       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.value       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.output.dense       ,		torch.tensor(_a		).view(-1       ,		_a		).contiguous().transpose(0       ,		1		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :List[str]     =       weights[0][0][0]
     lowercase_       :Optional[Any]     =       np.asarray(layer_norm_a[0]		)
     lowercase_       :Any     =       np.asarray(layer_norm_a[1]		)
     set_param(
         torch_block.attention.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # lsh weights + output
     lowercase_       :int     =       weights[0][1]
     if len(_a		) < 4:
          set_layer_weights_in_torch_lsh(_a       ,		torch_block.attention       ,		_a		)
     else:
          set_layer_weights_in_torch_local(_a       ,		torch_block.attention       ,		_a		)

     # intermediate weighs
     lowercase_       :Optional[int]     =       weights[2][0][1][2]

     # Chunked Feed Forward
     if len(_a		) == 4:
          lowercase_       :Tuple     =       intermediate_weights[2]

     # layernorm 2
     lowercase_       :int     =       np.asarray(intermediate_weights[0][0]		)
     lowercase_       :Any     =       np.asarray(intermediate_weights[0][1]		)
     set_param(
         torch_block.feed_forward.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # intermediate dense
     lowercase_       :Any     =       np.asarray(intermediate_weights[1][0]		)
     lowercase_       :int     =       np.asarray(intermediate_weights[1][1]		)
     set_param(
         torch_block.feed_forward.dense.dense       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)

     # intermediate out
     lowercase_       :Optional[int]     =       np.asarray(intermediate_weights[4][0]		)
     lowercase_       :Any     =       np.asarray(intermediate_weights[4][1]		)
     set_param(
         torch_block.feed_forward.output.dense       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Any     =       torch_model.reformer

     # word embeds
     lowercase_       :int     =       np.asarray(weights[1]		)
     set_param(
         torch_model_reformer.embeddings.word_embeddings       ,		torch.tensor(_a		)       ,		)

     if isinstance(weights[3]       ,		_a		):
          lowercase_       :Union[str, Any]     =       torch_model_reformer.embeddings.position_embeddings
          for emb_idx in range(len(position_embeddings.weights		)		):
               lowercase_       :Tuple     =       np.asarray(weights[3][emb_idx][0]		)
               assert (
                   position_embeddings.weights[emb_idx].shape == emb_weights.shape
               ), f"{position_embeddings[emb_idx]} emb does not match"
               lowercase_       :Optional[int]     =       nn.Parameter(torch.tensor(_a		)		)

     lowercase_       :Any     =       weights[5]
     assert len(torch_model_reformer.encoder.layers		) * 4 == len(
         _a		), "HF and trax model do not have the same number of layers"
     for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers		):
          lowercase_       :int     =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
          set_block_weights_in_torch(_a       ,		_a       ,		_a		)

     # output layer norm
     lowercase_       :Optional[Any]     =       np.asarray(weights[7][0]		)
     lowercase_       :int     =       np.asarray(weights[7][1]		)
     set_param(
         torch_model_reformer.encoder.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # output embeddings
     lowercase_       :Optional[int]     =       np.asarray(weights[9][0]		)
     lowercase_       :Union[str, Any]     =       np.asarray(weights[9][1]		)
     set_param(
         torch_model.lm_head.decoder       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							str:
     '''simple docstring'''





     lowercase_       :List[str]     =       ReformerConfig.from_json_file(_a		)
     print(f"Building PyTorch model from configuration: {config}"		)
     lowercase_       :List[str]     =       ReformerModelWithLMHead(_a		)

     with open(_a       ,		'''rb'''		) as f:
          lowercase_       :Dict     =       pickle.load(_a		)['''weights''']

     set_model_weights_in_torch(_a       ,		_a       ,		config.hidden_size		)

     # Save pytorch-model
     print(f"Save PyTorch model to {pytorch_dump_path}"		)
     torch.save(model.state_dict()       ,		_a		)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--config_file",
       default=None,
       type=str,
       required=True,
       help=(
           "The config json file corresponding to the pre-trained Reformer model. \n"
           "This specifies the model architecture."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   SCREAMING_SNAKE_CASE       :		Dict         =				parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)

