

import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        Adafactor,
        AdamW,
        get_constant_schedule,
        get_constant_schedule_with_warmup,
        get_cosine_schedule_with_warmup,
        get_cosine_with_hard_restarts_schedule_with_warmup,
        get_inverse_sqrt_schedule,
        get_linear_schedule_with_warmup,
        get_polynomial_decay_schedule_with_warmup,
    )





def 	__a     (      A__			:  str			,  A__			:  Any=10       ):
    SCREAMING_SNAKE_CASE										=   []
    for _ in range(A__       ):
        lrs.append(scheduler.get_lr()[0]       )
        scheduler.step()
    return lrs





def 	__a     (      A__			:  List[Any]			,  A__			:  Optional[Any]=10       ):
    SCREAMING_SNAKE_CASE										=   []
    for step in range(A__       ):
        lrs.append(scheduler.get_lr()[0]       )
        scheduler.step()
        if step == num_steps // 2:
            with tempfile.TemporaryDirectory() as tmpdirname:
                SCREAMING_SNAKE_CASE										=   os.path.join(A__			,  "schedule.bin"       )
                torch.save(scheduler.state_dict()			,  A__       )

                SCREAMING_SNAKE_CASE										=   torch.load(A__       )
                scheduler.load_state_dict(A__       )
    return lrs





@require_torch
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Tuple			):
       self.assertEqual(len(__lowerCamelCase			)     , len(__lowerCamelCase			)			)
       for a, b in zip(__lowerCamelCase     , __lowerCamelCase			):
           self.assertAlmostEqual(__lowerCamelCase     , __lowerCamelCase     , delta=__lowerCamelCase			)





   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   torch.tensor([0.1, -0.2, -0.1]     , requires_grad=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   torch.tensor([0.4, 0.2, -0.5]			)
       SCREAMING_SNAKE_CASE										=   nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       SCREAMING_SNAKE_CASE										=   AdamW(params=[w]     , lr=2e-1     , weight_decay=0.0			)
       for _ in range(100			):
           SCREAMING_SNAKE_CASE										=   criterion(__lowerCamelCase     , __lowerCamelCase			)
           loss.backward()
           optimizer.step()
           w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
           w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()     , [0.4, 0.2, -0.5]     , tol=1e-2			)





   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   torch.tensor([0.1, -0.2, -0.1]     , requires_grad=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   torch.tensor([0.4, 0.2, -0.5]			)
       SCREAMING_SNAKE_CASE										=   nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       SCREAMING_SNAKE_CASE										=   Adafactor(
           params=[w]     , lr=1e-2     , eps=(1e-30, 1e-3)     , clip_threshold=1.0     , decay_rate=-0.8     , betaa=__lowerCamelCase     , weight_decay=0.0     , relative_step=__lowerCamelCase     , scale_parameter=__lowerCamelCase     , warmup_init=__lowerCamelCase     , )
       for _ in range(1000			):
           SCREAMING_SNAKE_CASE										=   criterion(__lowerCamelCase     , __lowerCamelCase			)
           loss.backward()
           optimizer.step()
           w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
           w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()     , [0.4, 0.2, -0.5]     , tol=1e-2			)





@require_torch
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      nn.Linear(5_0					,       5_0		) if is_torch_available() else None
   lowerCamelCase__				=      AdamW(m.parameters()					,       lr=10.0		) if is_torch_available() else None
   lowerCamelCase__				=      1_0





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Tuple     , __lowerCamelCase					:			Any=None			):
       self.assertEqual(len(__lowerCamelCase			)     , len(__lowerCamelCase			)			)
       for a, b in zip(__lowerCamelCase     , __lowerCamelCase			):
           self.assertAlmostEqual(__lowerCamelCase     , __lowerCamelCase     , delta=__lowerCamelCase     , msg=__lowerCamelCase			)





   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   {"num_warmup_steps": 2, "num_training_steps": 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       SCREAMING_SNAKE_CASE										=   {
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {"num_warmup_steps": 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, "num_cycles": 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
               [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {"num_warmup_steps": 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   data

           SCREAMING_SNAKE_CASE										=   scheduler_func(self.optimizer     , **__lowerCamelCase			)
           self.assertEqual(len([scheduler.get_lr()[0]]			)     , 1			)
           SCREAMING_SNAKE_CASE										=   unwrap_schedule(__lowerCamelCase     , self.num_steps			)
           self.assertListAlmostEqual(
               __lowerCamelCase     , __lowerCamelCase     , tol=1e-2     , msg=f"failed for {scheduler_func} in normal scheduler"     , )

           SCREAMING_SNAKE_CASE										=   scheduler_func(self.optimizer     , **__lowerCamelCase			)
           if scheduler_func.__name__ != "get_constant_schedule":
               LambdaScheduleWrapper.wrap_scheduler(__lowerCamelCase			)  # wrap to test picklability of the schedule
           SCREAMING_SNAKE_CASE										=   unwrap_and_save_reload_schedule(__lowerCamelCase     , self.num_steps			)
           self.assertListEqual(__lowerCamelCase     , __lowerCamelCase     , msg=f"failed for {scheduler_func} in save and reload"			)





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   fn





   def __call__(       self					:			List[Any]     , *__lowerCamelCase					:			Optional[Any]     , **__lowerCamelCase					:			List[Any]			):
       return self.fn(*__lowerCamelCase     , **__lowerCamelCase			)





   @classmethod
   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Dict			):
       SCREAMING_SNAKE_CASE										=   list(map(self     , scheduler.lr_lambdas			)			)







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCamelCase      :       List[str]    =						'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
lowerCamelCase      :       str    =						[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
lowerCamelCase      :       List[Any]    =						dict(zip(vocab, range(len(vocab))))
lowerCamelCase      :       List[str]    =						['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
 lowerCamelCase      :       int    =						Path(tmpdirname)
 lowerCamelCase      :       Optional[int]    =						build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
 lowerCamelCase      :       List[str]    =						build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
 lowerCamelCase      :       int    =						build_dir / VOCAB_FILES_NAMES['''merges_file''']
 with open(src_vocab_file, '''w''') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, '''w''') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, '''w''') as fp:
  fp.write('''\n'''.join(merges))

 lowerCamelCase      :       Dict    =						FSMTTokenizer(
     langs=['''en''', '''ru'''],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

lowerCamelCase      :       int    =						FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

lowerCamelCase      :       Any    =						FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
lowerCamelCase      :       int    =						tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase      :       Dict    =						tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru






